Methods and systems for estimating a remaining useful life of an asset

ABSTRACT

Methods and systems are provided for monitoring a health of a vehicle component. In one embodiment, a method is provided, comprising dividing a population of vehicles of a connected vehicle population into a plurality of vehicle classes; for each vehicle class of the plurality of vehicle classes, training a class-specific model of the vehicle class to predict a health status variable of a vehicle component included in the vehicle class based on labelled data from historic databases and calibration data; and for each vehicle class of the plurality of vehicle classes, using a first Federated Learning strategy to request local model data from each vehicle of a plurality of vehicles of the vehicle class; receive the local model data from the plurality of vehicles; update the class-specific model based on the received local model data; and send updated parameters of the class-specific model to vehicles included in the vehicle class.

FIELD

The present description relates generally to methods and systems formonitoring the health of physical assets, and more specifically, toestimating a remaining useful life of vehicle components.

BACKGROUND/SUMMARY

Predictive maintenance, also known as condition-based maintenance, is astrategy that involves continually monitoring the condition of assets(e.g., vehicle components) to determine maintenance actions needed to betaken at certain times. Prognostics, anomaly detection (AD), andremaining useful life (RUL) prediction systems may continuously monitorthe health of the assets, and provide notifications when servicing isrecommended. An RUL prediction system may include one or more RULpredictive models that estimate an RUL of an asset based on data of theasset collected over time.

In a traditional vehicle setting, a local RUL predictive model isembedded in the vehicle and relies on local or onboard information toestablish asset health status. In a connected vehicle system, thephysical assets may also be connected to a cloud-based health monitoringsystem and to other physical assets via an Internet of Things (IoT)framework, where each asset may transmit to and receive information fromthe cloud-based health monitoring system and the other assets. A masterRUL predictive model may be maintained at a central, cloud-based server,which may make predictions based on received asset populationinformation from a plurality of similar vehicles operating in the field,as well as information from manufacturers, including engineeringinformation, repair information, warranty information, and the like. Forexample, Barfield et. al in U.S. Pat. No. 9,881,428 teaches predictingpotential component degradations in a vehicle by evaluating cloud-baseddata from a plurality of vehicles.

However, the inventors herein have recognized potential issues withusing connected vehicle data to maintain master and local RUL models.Transmitting vehicle component data to the cloud-based server may becostly in terms of bandwidth, may take too much time for applicationsthat rely on low latency inference, and/or may result in data leakagesand compromised data privacy.

One option for addressing these issues is to use a distributed learningapproach such as Federated Learning (FL) to break a model updatingprocedure into a plurality of learning sessions, where during eachlearning session a master RUL model is updated based on data from arandomly selected subset of local RUL models, with updates beingpropagated to remaining local RUL models over time. However, RUL modelaccuracy with FL may depend on an independent and identical distribution(IID) of RUL model data, and an implicit heterogeneity of RUL model datadue to parameters (weights) of local RUL models varying widely indifferent operating environments may lead to poor performance.Additionally, training of RUL models may be complicated by a difficultyof obtaining ground truth information, which may only be availableperiodically when a degradation of a component is detected.

In one example, the issues described above may be addressed by a method,comprising dividing a population of vehicles of a connected vehiclepopulation into a plurality of vehicle classes; for each vehicle classof the plurality of vehicle classes, training a class-specific model ofthe vehicle class to predict a health status variable of a vehiclecomponent included in the vehicle class based on labelled data fromhistoric databases and calibration data; and for each vehicle class ofthe plurality of vehicle classes, using a first Federated Learning (FL)strategy to request local model data from each vehicle of a plurality ofvehicles of the vehicle class; receive the local model data from theplurality of vehicles; update the class-specific model of the vehicleclass based on the received local model data; and send updatedparameters of the updated, class-specific model to vehicles included inthe vehicle class and further send instructions to retrain local modelsof the vehicles with the updated parameters.

As one example, a vehicle in a fleet of vehicles may include a fuelinjector, and a corresponding local model for predicting an RUL of thefuel injector. A cloud-based health monitoring system connected to thefleet may divide the fleet into a plurality of vehicle classes, whereeach vehicle class has a class-specific RUL model that predicts the RULof fuel injectors in the respective vehicle class. The class-specificRUL model may be trained on labelled fuel injector data of therespective vehicle class including ground truth degradation data. Asfuel injector degradation data is collected at vehicles of the fleet andthe local RUL models are updated accordingly, parameters of theclass-specific RUL model of the fuel injector may be updated via an FLstrategy, whereby local RUL model data may be iteratively requested fromrandomly selected portions of vehicles of the respective vehicle class,used to update the parameters of the class-specific RUL model of thefuel injector, which may in turn be sent back to the randomly selectedportion of vehicles of the respective vehicle class to update the localRUL models of the fuel injectors of the randomly selected portion ofvehicles. The FL strategy may end when local RUL model parameters of arandomly selected portion of vehicles converge with the parameters ofthe class-specific RUL model. Additionally, a master RUL model of thefuel injectors of the fleet of vehicles may be updated based on theupdated parameters of the class-specific RUL models of the plurality ofvehicle classes.

In this way, an accuracy of RUL models at a fleet level, a class level,and a vehicle level may all be increased. By increasing the accuracy ofthe RUL models at a fleet level, a class level, and a vehicle level,fuel injector degradations at vehicles may be more accurately predicted;differences between fuel injector lifetimes of different vehicle classesmay be identified, and fuel injector design changes and warrantyinformation at a manufacturer of the fuel injector may be betterinformed. An additional advantage of the methods and systems disclosedherein is that the fleet of vehicles may be partitioned into the vehicleclasses based on either RUL model performance data or vehicle populationdistribution data, and a topology or number of the vehicle classes maybe adjusted as new data becomes available.

It should be understood that the summary above is provided to introducein simplified form a selection of concepts that are further described inthe detailed description. It is not meant to identify key or essentialfeatures of the claimed subject matter, the scope of which is defineduniquely by the claims that follow the detailed description.Furthermore, the claimed subject matter is not limited toimplementations that solve any disadvantages noted above or in any partof this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a schematic of an engine included in a hybridvehicle.

FIG. 2 schematically shows an example vehicle propulsion system.

FIG. 3A is a graph illustrating a health index of a vehicle component ofa first vehicle class.

FIG. 3B is a graph illustrating a health index of a vehicle component ofa second vehicle class.

FIG. 3C is a graph illustrating a health index of a vehicle component ofdifferent vehicle subclasses of a vehicle class.

FIG. 4 illustrates an example system for updating RUL models utilizingconnected vehicle data.

FIG. 5 is a flowchart that illustrates an exemplary high-level methodfor updating RUL models using federated learning, based on connectedvehicle data.

FIG. 6 is a flowchart that illustrates an exemplary method for updatingan RUL model of a vehicle component.

FIG. 7 is a flowchart that illustrates an exemplary method forinitiating a new RUL model learning session for a vehicle component.

FIG. 8 is a flowchart that illustrates a first exemplary method forpartitioning a vehicle population into vehicle classes.

FIG. 9 is a flowchart that illustrates a second exemplary method forpartitioning a vehicle population into vehicle classes.

FIG. 10A shows an example vehicle population divided into subclasses.

FIG. 10B shows a change in a vehicle population distribution as newsamples are added to the population.

FIG. 10C shows how vehicle subclasses are adjusted based on new sampledegradation data using unsupervised learning.

FIG. 11 is a graph that shows a decrease in a health index of a vehiclecomponent over a lifetime of the vehicle component.

DETAILED DESCRIPTION

The following description relates to systems and methods for maintainingand continuously updating remaining useful life (RUL) models of avehicle component based on data received from a vehicle population. Thevehicle population may be a connected vehicle population, where thecontroller may communicate, via a wireless modem of the vehicle, with aplurality of other vehicles of the connected vehicle population and oneor more cloud-based services, such as a cloud-based health monitoringsystem. As described in greater detail below, the RUL models may betrained and updated based on data received from other vehicles of theconnected vehicle population that include the vehicle component.

The RUL models may be created by a manufacturer of the vehicle prior todeployment. A local RUL model may predict an RUL of specific componentat a vehicle of the connected vehicle system. The local RUL model may beused to reduce a probability that the asset will degrade in the field,and to estimate when the vehicle should be brought in for servicing. Amaster RUL model may predict an overall RUL of the vehicle componentacross the connected vehicle system. The master RUL model may be used,for example, to prepare and manage recall campaigns, inform designchanges, optimize fleet maintenance schedules while minimizing fleetdowntime and cost of ownership.

Additionally, a class-specific RUL model may predict an RUL ofcomponents of vehicles belonging to a vehicle class of the connectedvehicle system. For example, due to variations in humidity, a componentof a type of vehicle operated in Michigan may degrade at a differentrate than a same component of the same type of vehicle operated inFlorida. Components may also degrade at a different rate due to driverbehavior, traffic patterns, and/or other operational factors. Due tovariations in deterioration between vehicles due to factors such ashumidity, altitude, road conditions, operating conditions, etc.,connected vehicle data over a vehicle population may become unbalancedor skewed over time. Therefore, to maintain and/or increase an RULprediction accuracy, different class-specific RUL models may be createdfor different vehicle classes, where a variation in factors leading todeterioration may be minimized within the different vehicle classes. Toincrease an accuracy of the RUL models, methods are proposed to trainand continually update the RUL models based on data received from theconnected vehicle system.

A vehicle may include an engine, such as the engine depicted in FIG. 1 ,within a vehicle propulsion system such as the vehicle propulsion systemof FIG. 2 . A component of the vehicle may have an RUL, which may beupdated based on one or more RUL models. The one or more RUL models maybe specific to a component of a class and/or subclass of the vehicle, asshown in FIGS. 3A-3C, where the classes and/or subclasses may be basedon variables including a geographical location of the vehicle and adriving style of a driver of the vehicle. The one or more RUL models maybe updated based on RUL and/or degradation data of the componentcollected from one or more manufacturing databases and/or a connectedvehicle system via a cloud-based RUL server system, such as the RULserver system 401 of FIG. 4 , at various points in a lifetime of thecomponent as shown in FIG. 11 . The RUL models may be based on vehicleclasses, which may be established using a first partitioning method 800of FIG. 8 or a second partitioning method 900 of FIG. 9 , as graphicallyindicated in FIGS. 10A, 10B, and 10C. The RUL models may be updatedbased on federated learning, in accordance with a procedure such as theprocedure described by method 500 of FIG. 5 . An updating of an RULmodel may be carried out as shown in FIG. 6 . Updating of RUL models mayoccur during model learning sessions of the federated learningprocedure, as described in method 700 of FIG. 7 .

FIG. 1 shows a schematic depiction of a hybrid vehicle system 6 that canderive propulsion power from engine system 8 and/or an on-board energystorage device. An energy conversion device, such as a generator, may beoperated to absorb energy from vehicle motion and/or engine operation,and then convert the absorbed energy to an energy form suitable forstorage by the energy storage device.

Engine system 8 may include an engine 10 having a plurality of cylinders30. Engine 10 includes an engine intake 23 and an engine exhaust 25.Engine intake 23 includes an air intake throttle 62 fluidly coupled tothe engine intake manifold 44 via an intake passage 42. Air may enterintake passage 42 via air filter 52. Engine exhaust 25 includes anexhaust manifold 48 leading to an exhaust passage 35 that routes exhaustgas to the atmosphere. Engine exhaust 25 may include one or moreemission control devices 70 mounted in a close-coupled position or in afar underbody position. The one or more emission control devices mayinclude a three-way catalyst, lean NOx trap, diesel particulate filter,oxidation catalyst, etc. It will be appreciated that other componentsmay be included in the engine such as a variety of valves and sensors,as further elaborated in herein. In some embodiments, wherein enginesystem 8 is a boosted engine system, the engine system may furtherinclude a boosting device, such as a turbocharger (not shown).

Vehicle system 6 may further include control system 14. Control system14 is shown receiving information from a plurality of sensors 16(various examples of which are described herein) and sending controlsignals to a plurality of actuators 81 (various examples of which aredescribed herein). As one example, sensors 16 may include exhaust gassensor 126 located upstream of the emission control device, temperaturesensor 128, and pressure sensor 129. Other sensors such as additionalpressure, temperature, air/fuel ratio, and composition sensors may becoupled to various locations in the vehicle system 6. As anotherexample, the actuators may include the throttle 62.

Controller 12 may be configured as a conventional microcomputerincluding a microprocessor unit, input/output ports, read-only memory,random access memory, keep alive memory, a controller area network (CAN)bus, etc. Controller 12 may be configured as a powertrain control module(PCM). The controller may be shifted between sleep and wake-up modes foradditional energy efficiency. The controller may receive input data fromthe various sensors, process the input data, and trigger the actuatorsin response to the processed input data based on instruction or codeprogrammed therein corresponding to one or more routines.

In some examples, hybrid vehicle 6 comprises multiple sources of torqueavailable to one or more vehicle wheels 59. In other examples, vehicle 6is a conventional vehicle with only an engine, or an electric vehiclewith only electric machine(s). In the example shown, vehicle 6 includesengine 10 and an electric machine 51. Electric machine 51 may be a motoror a motor/generator. A crankshaft of engine 10 and electric machine 51may be connected via a transmission 54 to vehicle wheels 59 when one ormore clutches 56 are engaged. In the depicted example, a first clutch 56is provided between a crankshaft and the electric machine 51, and asecond clutch 56 is provided between electric machine 51 andtransmission 54. Controller 12 may send a signal to an actuator of eachclutch 56 to engage or disengage the clutch, so as to connect ordisconnect crankshaft from electric machine 51 and the componentsconnected thereto, and/or connect or disconnect electric machine 51 fromtransmission 54 and the components connected thereto. Transmission 54may be a gearbox, a planetary gear system, or another type oftransmission. The powertrain may be configured in various mannersincluding as a parallel, a series, or a series-parallel hybrid vehicle.

Electric machine 51 receives electrical power from a traction battery 61to provide torque to vehicle wheels 59. Electric machine 51 may also beoperated as a generator to provide electrical power to charge battery61, for example during a braking operation.

FIG. 2 illustrates an example vehicle propulsion system 200 which is anon-limiting example of hybrid vehicle system 6 of FIG. 1 . Vehiclepropulsion system 200 includes a fuel burning engine 210 and a motor220. As a non-limiting example, engine 210 comprises an internalcombustion engine and motor 220 comprises an electric motor. Engine 210may be a non-limiting example of engine 10 of FIG. 1 and motor 220 maybe a non-limiting example of electric machine 51 of FIG. 1 . Motor 220may be configured to utilize or consume a different energy source thanengine 210. For example, engine 210 may consume a liquid fuel (e.g.,gasoline) to produce an engine output while motor 220 may consumeelectrical energy to produce a motor output. As such, a vehicle withpropulsion system 200 may be referred to as a hybrid electric vehicle(HEV).

Vehicle propulsion system 200 may utilize a variety of differentoperational modes depending on operating conditions encountered by thevehicle propulsion system. Some of these modes may enable engine 210 tobe maintained in an off state (e.g., set to a deactivated state) wherecombustion of fuel at the engine is discontinued. For example, underselect operating conditions, motor 220 may propel the vehicle via drivewheel 230 as indicated by arrow 222 while engine 210 is deactivated,which may herein be referred to as an electric-only operation.

During other operating conditions, engine 210 may be set to adeactivated state (as described above) while motor 220 may be operatedto charge energy storage device 250. For example, motor 220 may receivewheel torque from drive wheel 230 as indicated by arrow 222 where themotor may convert the kinetic energy of the vehicle to electrical energyfor storage at energy storage device 250 as indicated by arrow 224. Thisoperation may be referred to as regenerative braking of the vehicle.Thus, motor 220 can provide a generator function in some examples.However, in other examples, generator 260 may instead receive wheeltorque from drive wheel 230, where the generator may convert the kineticenergy of the vehicle to electrical energy for storage at energy storagedevice 250 as indicated by arrow 262. In some examples, the engine 210may deactivate during regenerative braking and traction at the drivewheel 230 may be negative, such that the motor 220 may spin in reverseand recharge the energy storage device 250. Thus, regenerative brakingmay be distinguished from an electric-only operation, where the motor220 may provide positive traction at the drive wheel 230, therebydecreasing a SOC of the energy storage device 250 while the engine 210is deactivated.

During still other operating conditions, engine 210 may be operated bycombusting fuel received from fuel system 240 as indicated by arrow 242.For example, engine 210 may be operated to propel the vehicle via drivewheel 230 as indicated by arrow 212 while motor 220 is deactivated, suchas during a charge-sustaining operation. During other operatingconditions, both engine 210 and motor 220 may each be operated to propelthe vehicle via drive wheel 230 as indicated by arrows 212 and 222,respectively. A configuration where both the engine and the motor mayselectively propel the vehicle may be referred to as a parallel typevehicle propulsion system or a hybrid propulsion. Note that in someexamples, motor 220 may propel the vehicle via a first set of drivewheels and engine 210 may propel the vehicle via a second set of drivewheels.

In other examples, vehicle propulsion system 200 may be configured as aseries type vehicle propulsion system, whereby the engine does notdirectly propel the drive wheels. Rather, engine 210 may be operated bypower motor 220, which may in turn propel the vehicle via drive wheel230 as indicated by arrow 222. For example, during select operatingconditions, engine 210 may drive generator 260 as indicated by arrow216, which may in turn supply electrical energy to one or more of motor220 as indicated by arrow 214 or energy storage device 250 as indicatedby arrow 262. As another example, engine 210 may be operated to drivemotor 220 which may in turn provide a generator function to convert theengine output to electrical energy, where the electrical energy may bestored at energy storage device 250 for later use by the motor.

In still other examples, which will be discussed in further detailbelow, motor 220 may be configured to rotate engine unfueled in aforward (e.g. default orientation) or reverse orientation, using energyprovided via energy storage device 250, exemplified by arrow 286.

Fuel system 240 may include one or more fuel storage tanks 244 forstoring fuel on-board the vehicle. For example, fuel tank 244 may storeone or more liquid fuels, including but not limited to: gasoline,diesel, and alcohol fuels. In some examples, the fuel may be storedon-board the vehicle as a blend of two or more different fuels. Forexample, fuel tank 244 may be configured to store a blend of diesel andbiodiesel, gasoline and ethanol (e.g., E10, E85, etc.) or a blend ofgasoline and methanol (e.g., M10, M85, etc.), whereby these fuels orfuel blends may be delivered to engine 210 as indicated by arrow 242.Still other suitable fuels or fuel blends may be supplied to engine 210,where they may be combusted at the engine to produce an engine output.The engine output may be utilized to propel the vehicle as indicated byarrow 212 or to recharge energy storage device 250 via motor 220 orgenerator 260.

In some examples, energy storage device 250 may be configured to storeelectrical energy that may be supplied to other electrical loadsresiding on-board the vehicle (other than the motor), including cabinheating and air conditioning, engine starting, headlights, cabin audioand video systems, etc. As a non-limiting example, energy storage device250 may include one or more batteries and/or capacitors. In someexamples, increasing the electrical energy supplied from the energystorage device 250 may decrease an electric-only operation range, aswill be described in greater detail below.

Control system 290 may communicate with one or more of engine 210, motor220, fuel system 240, energy storage device 250, and generator 260. Insome examples, control system 290 may be used similarly to controller 12of FIG. 1 . Control system 290 may receive sensory feedback informationfrom one or more of engine 210, motor 220, fuel system 240, energystorage device 250, and generator 260. Further, control system 290 maysend control signals to one or more of engine 210, motor 220, fuelsystem 240, energy storage device 250, and generator 260 responsive tothis sensory feedback. In some examples, control system 290 may receivean indication of an operator requested output of the vehicle propulsionsystem from a vehicle operator 202. For example, control system 290 mayreceive sensory feedback from pedal position sensor 294 whichcommunicates with pedal 292. Pedal 292 may refer schematically to abrake pedal and/or an accelerator pedal. Furthermore, in some examplescontrol system 290 may be in communication with a remote engine startreceiver 295 (or transceiver) that receives wireless signals 206 from akey fob 204 having a remote start button 205. In other examples (notshown), a remote engine start may be initiated via a cellular telephone,or smartphone based system where a user's cellular telephone sends datato a server and the server communicates with the vehicle to start theengine.

In some examples, additionally or alternatively, the vehicle propulsionsystem 200 may be configured to operate autonomously (e.g., without ahuman vehicle operator). As such, the control system 290 may determineone or more desired operating engine conditions based on estimatedcurrent driving conditions.

Energy storage device 250 may periodically receive electrical energyfrom a power source 280 residing external to the vehicle (e.g., not partof the vehicle) as indicated by arrow 284. As a non-limiting example,vehicle propulsion system 200 may be configured as a plug-in hybridelectric vehicle (HEV), whereby electrical energy may be supplied toenergy storage device 250 from power source 280 via an electrical energytransmission cable 282. During a recharging operation of energy storagedevice 250 from power source 280, electrical transmission cable 282 mayelectrically couple energy storage device 250 and power source 280.While the vehicle propulsion system is operated to propel the vehicle,electrical transmission cable 282 may disconnect between power source280 and energy storage device 250. Control system 290 may identifyand/or control the amount of electrical energy stored at the energystorage device, which may be referred to as the state of charge (SOC).

In other examples, electrical transmission cable 282 may be omitted,where electrical energy may be received wirelessly at energy storagedevice 250 from power source 280. For example, energy storage device 250may receive electrical energy from power source 280 via one or more ofelectromagnetic induction, radio waves, and electromagnetic resonance.As such, it should be appreciated that any suitable approach may be usedfor recharging energy storage device 250 from a power source that doesnot comprise part of the vehicle. In this way, motor 220 may propel thevehicle by utilizing an energy source other than the fuel utilized byengine 210.

Fuel system 240 may periodically receive fuel from a fuel sourceresiding external to the vehicle. As a non-limiting example, vehiclepropulsion system 200 may be refueled by receiving fuel via a fueldispensing device 270 as indicated by arrow 272. In some examples, fueltank 244 may be configured to store the fuel received from fueldispensing device 270 until it is supplied to engine 210 for combustion.In some examples, control system 290 may receive an indication of thelevel of fuel stored at fuel tank 244 via a fuel level sensor. The levelof fuel stored at fuel tank 244 (e.g., as identified by the fuel levelsensor) may be communicated to the vehicle operator, for example, via afuel gauge or indication in a vehicle instrument panel 296.

The vehicle propulsion system 200 may also include an ambienttemperature/humidity sensor 298, and a roll stability control sensor,such as a lateral and/or longitudinal and/or yaw rate sensor(s) 299. Thevehicle instrument panel 296 may include indicator light(s) and/or atext-based display in which messages are displayed to an operator. Thevehicle instrument panel 296 may also include various input portions forreceiving an operator input, such as buttons, touch screens, voiceinput/recognition, etc. For example, the vehicle instrument panel 296may include a refueling button 297 which may be manually actuated orpressed by a vehicle operator to initiate refueling. For example, asdescribed in more detail below, in response to the vehicle operatoractuating refueling button 297, a fuel tank in the vehicle may bedepressurized so that refueling may be performed.

Control system 290 may be communicatively coupled to other vehicles orinfrastructures using appropriate communications technology, as is knownin the art. For example, control system 290 may be coupled to othervehicles or infrastructures via a wireless network 231, which maycomprise Wi-Fi, Bluetooth, a type of cellular service, a wireless datatransfer protocol, and so on. Control system 290 may broadcast (andreceive) information regarding vehicle data, vehicle diagnostics,traffic conditions, vehicle location information, vehicle operatingprocedures, etc., via vehicle-to-vehicle (V2V),vehicle-to-infrastructure-to-vehicle (V2I2V), and/orvehicle-to-infrastructure (V2I or V2X) technology. The communication andthe information exchanged between vehicles can be either direct betweenvehicles, or can be multi-hop. In some examples, longer rangecommunications (e.g. WiMax) may be used in place of, or in conjunctionwith, V2V, or V2I2V, to extend the coverage area by a few miles. Instill other examples, vehicle control system 290 may be communicativelycoupled to other vehicles or infrastructures via a wireless network 231and the internet (e.g. cloud), as is commonly known in the art. Oneexample of a V2V communication device may includededicated-short-range-communication (DSRC) network which may allowvehicles within a threshold proximity (e.g., 5,000 feet) to communicate(e.g., transfer information) free of an internet connection.

The wireless network 231 may include one or more computing systems(e.g., servers) including memory and one or more processors. The memorymay be configured to store various AD/RUL models as described herein, aswell as various data provided thereto, including vehicleoperational/sensor data obtained from multiple vehicles. The processormay execute the instructions stored in memory in order to enter thevehicle operational/sensor data into and periodically update the variousmodels, as described below.

Vehicle propulsion system 200 may also include an on-board navigationsystem 232 (for example, a Global Positioning System) that an operatorof the vehicle may interact with. The navigation system 232 may includeone or more location sensors for assisting in estimating vehicle speed,vehicle altitude, vehicle position/location, etc. This information maybe used to infer engine operating parameters, such as local barometricpressure. As discussed above, control system 290 may further beconfigured to receive information via the internet or othercommunication networks. Information received from the GPS may becross-referenced to information available via the internet to determinelocal weather conditions, local vehicle regulations, etc.

In some examples, vehicle propulsion system 200 may include one or moreonboard cameras 235. Onboard cameras 235 may communicate photos and/orvideo images to control system 290, for example. Onboard cameras may insome examples be utilized to record images within a predetermined radiusof the vehicle, for example. The onboard cameras 235 may be arranged onan exterior surface of the vehicle so that an area surrounding and/oradjacent to the vehicle may be visualized.

Vehicles have components that degrade under a plurality of conditions,and manufacturers may develop models (including RUL and AD models) thatpredict when vehicle components may degrade. The models may be used tonotify the operator so the vehicle may be serviced. However, thethresholds may be based on empirical models tested on new vehicles, ortested under limited conditions, and the like, so the models can resultin false positive or false negative determinations of degradation. Falsepositives and false negatives provide inconveniences for the vehicleoperator, including but not limited to complete component degradationwithout notification, consistently incorrect degradation notifications,and frequent repairs. To increase an accuracy of the models, a vehiclepopulation may be divided into a plurality of classes and sub-classes,where a different model may be associated with each class and/orsub-class of the vehicle population. The classes and sub-classes may bebased on characteristics of the vehicle or a driver of the vehicle,including vehicle make, model, year, type, geographical location,driving style, etc.

For example, referring to FIG. 3A, a first health index graph 300 showsa health index of a vehicle component in vehicles of a first vehicleclass, where the first vehicle class includes vehicles of a particulartype or group of types (e.g., pickup trucks, SUVs, a particularmake/model SUV, etc.) driven in Michigan. In graph 300, plot 302 shows afirst decrease in the health index of the vehicle component as mileageincreases, for all vehicles in the first vehicle class. Plot 302 mayshow an average health index of the vehicle component across allvehicles of the first vehicle class (e.g., similar vehicles includingthe component that are driven in Michigan), calculated based on datacollected from the vehicles of the first vehicle class and stored andprocessed in a cloud-based health maintenance system 304. For vehiclesdriven in Michigan, the component may experience extreme temperatureand/or weather differences between winter and summer. As a result of theextreme weather differences, the health index of the vehicle componentin the first vehicle class may decrease rapidly in a middle portion 306of an average useful life of the vehicle component.

FIG. 3B shows a second health index graph 330, indicating a health indexof an identical component in a vehicle of a second vehicle class, wherethe second vehicle class includes vehicles of the particular type orgroup of types that are driven in Florida. In graph 330, plot 332 showsa second decrease in the health index of the vehicle component asmileage increases, for all vehicles in the second vehicle class. Plot302 may show the average health index of the vehicle component acrossall vehicles of the second vehicle class, calculated based on datacollected from the vehicles of the second vehicle class and stored andprocessed in the cloud-based health maintenance system 304. For vehiclesdriven in Florida, the component may not experience extreme temperatureand/or weather differences between winter and summer. As a result of notexperiencing the extreme weather differences, the health index of thevehicle component in the second class may decrease rapidly in a middleportion 336 of the average useful life of the vehicle component. Thus,due to differences in component degradation based on geographicallocation, a first model may be used to predict an RUL of the vehiclecomponent of the first vehicle class, while a second model may be usedto predict an RUL of the vehicle component of the second vehicle class.

In FIG. 3C, a third health index graph 360 shows the plot 302 of FIG.3A, of the first vehicle class corresponding to vehicles driven inMichigan, with two additional plots 364 and 366. The two additionalplots 364 and 366 may show the average health index of the vehiclecomponent across vehicles of different sub-classes of the first vehicleclass. For example, the first vehicle class may be further divided intoa first sub-class 368 and a second sub-class 370 based on a drivingstyle of drivers of the vehicles in the first vehicle class. The firstsub-class 368 may include drivers who have an aggressive or impatientdriving style, while the second sub-class 370 may include drivers whohave a cautious driving style. Thus, plot 364 may show the averagehealth index of the vehicle component of vehicles of the first sub-class368 over an average mileage of the vehicles of first sub-class 368,while plot 366 may show the average health index of the vehiclecomponent of vehicles of the second sub-class 370 over an averagemileage of the vehicles of second sub-class 370. As can be seen by plots364 and 366, throughout a useful life of the vehicle component, thehealth index of the vehicle component may be maintained at a higherlevel for vehicles of the second vehicle sub-class 370 (e.g., thecautious drivers) than the first vehicle sub-class 368 (e.g., theaggressive drivers). As a result, different RUL or AD models may be usedfor the second vehicle sub-class 370 and the first vehicle sub-class368.

Each of the plots 302, 364, and 366 may be generated from a plurality ofindividual data points 372, where each individual data point 372 may bean assessment of a health index of a component of an individual vehicleof the corresponding vehicle class or subclass. A dashed portion of eachplot, such as the dashed portion 374 of plot 366, may indicate apredicted health index for the component of the vehicle class/sub-classwhen no individual data points 372 are available.

In this way, a different RUL model may be assigned to each combinationof vehicle class and vehicle sub-class. For example, a first RUL modelmay be assigned to the first sub-class 368 of the first vehicle class(e.g., representing aggressive drivers in Michigan); a second RUL modelmay be assigned to the second sub-class 370 of the first vehicle class(e.g., representing cautious drivers in Michigan); a third RUL model maybe assigned to the first sub-class 368 of the second vehicle class(e.g., representing aggressive drivers in Florida); and a fourth RULmodel may be assigned to the second sub-class 370 of the second vehicleclass (e.g., representing cautious drivers in Florida). By maintainingand updating different RUL models for each vehicle class and sub-class,more accurate RUL predictions may be made for each vehicle class andsub-class.

Turning now to FIG. 4 , a system 400 is shown for RUL predictionleveraging connected vehicle data and resources from a cloud network410. System 400 may facilitate more accurate RUL modeling by updatingRUL models when new data is received, thereby reducing false positivesand false negatives. When the RUL models of system 400 are applied tolocal vehicle components, vehicle operators may receive more accurateinformation regarding the health of vehicle components in the localvehicle, which may lead to a reduction in repair costs over time.

System 400 includes a server system 401. Server system 401 may includeresources (e.g., memory, processor(s)) that may be allocated to store aplurality of RUL models and store and execute instructions in order toupdate one or more of the RUL models based on vehicle data collectedfrom a plurality of vehicles. Server system 401 may include acommunication module, memory, and processor(s) (not shown in FIG. 4 ) tostore and update the RUL models described herein. The communicationmodule may facilitate transmission of electronic data within and/oramong one or more systems. Communication via the communication modulecan be implemented using one or more protocols. The communication modulecan be a wired interface (e.g., a data bus, a Universal Serial Bus (USB)connection, etc.) and/or a wireless interface (e.g., radio frequency,infrared, near field communication (NFC), etc.). For example, thecommunication module may communicate via wired local area network (LAN),wireless LAN, wide area network (WAN), etc. using any past, present, orfuture communication protocol (e.g., BLUETOOTH™, USB 2.0, USB 3.0,etc.).

The memory may include one or more data storage structures, such asoptical memory devices, magnetic memory devices, or solid-state memorydevices, for storing programs and routines executed by the processor(s)of the server system 401 to carry out various functionalities disclosedherein. The memory may include any desired type of volatile and/ornon-volatile memory such as, for example, static random access memory(SRAM), dynamic random access memory (DRAM), flash memory, read-onlymemory (ROM), etc. The processor(s) may be any suitable processor,processing unit, or microprocessor, for example. The processor(s) may bea multi-processor system, and, thus, may include one or more additionalprocessors that are identical or similar to each other and that arecommunicatively coupled via an interconnection bus.

System 400 includes one or more databases storing data that may be usedfor initializing and then updating one or more RUL models. As shown,system 400 includes a first database 402, a second database 404, and athird database 406. In an embodiment, first database 402 is anengineering database, storing manufacturing data about vehiclecomponents, including but not limited to component historical data,vehicle historical data, and manufacturer default data for predictivemodels. In an embodiment, second database 404 is a warranty database,storing warranty data about vehicle components, including but notlimited to expected lifespan data, RUL time data, and historicalwarranty data. In an embodiment, third database 406 is a dealership andrepairs database, storing data about vehicle components, including butnot limited to component repair frequency data, component health databefore and after repairs, and dealership test drive data. The databasesmay include cause and effect data as it relates to vehicle component RULprediction to aid predictive models in monitoring vehicle componenthealth. Additionally or alternatively, more or fewer databases may beused in RUL prediction for vehicle components. In alternate embodiments,databases 402, 404, and 406 may include different types of data such asdriver behavior data and/or geographical data.

Server system 401 may aggregate the data from databases 402, 404, and406 to assess preliminary or base predictive models that may bedeveloped based on initial testing data and historical data fromdevelopers and manufacturers and stored or deployed by the server system401. The server system 401 may compare a performance of the basepredictive models to current connected vehicle data to detect new databehaviors, emerging trends in component data, and the like. The serversystem 401, via a network 410, may communicate data, including but notlimited to the base predictive models and aggregated data from databases402, 404, and 406, over the air to update vehicle systems in a connectedvehicle population and to update a development database 408.

The development database 408 may store updates to the deployed modelsand manufacturers may receive updates from the development database 408.Development database 408 may store vehicle component data from one ormore pluralities of vehicle populations in a connected system, such asvia network 410. In one example, development database 408 represents aplurality of databases from different manufacturers. A vehicle mayinclude components from multiple manufacturers, in which eachmanufacturer has a database of components. Development database 408 mayfurther store data that predictive model developers use to develop andupdate RUL models, as well as storing the models themselves. Using thedata from development database 408, developers may generate a pluralityof RUL models, including but not limited to empirical models, physicalmodels, and machine learning models. The models and initial metrics maybe tested using known test cases, conditions, and noise factors. Whendevelopers are using data from development database 408 to update RULmodels, data relating to repairs, warranties, recalls, fines, and thelike may be used to test the updated models for redeployment.

The server system 401, databases 402, 404, and 406, the developmentdatabase 408, and a plurality of vehicles 420 may communicate over asuitable network, such as network 410. Further, one or more of thedevices described herein may be implemented over a cloud or othercomputer network. For example, server system 401 is shown in FIG. 4 asconstituting a single entity, but it is to be understood that serversystem 401 may be distributed across multiple devices, such as acrossmultiple servers.

An example of a deployed RUL model (e.g., deployed on one or more of theplurality of vehicles 420) is visually represented in FIG. 4 asdeployment RUL model 430, which may be modeled upon initial deploymentof models from the development database 408. For example, RUL model 430may be created for a fuel injector, in which historical data of the fuelinjector in a connected vehicle population is modeled againstmanufacturer data for the fuel injector to predict an RUL of the fuelinjector. The RUL model 430 may then be deployed to the vehicles thatcorrespond to the components with predictive models for healthmonitoring.

Server system 401 may receive updates of data aggregated from theplurality of vehicles 420. The aggregated data may be used to updatemaster and/or class-specific RUL models of vehicle components.Distribution data 440 is a visualization of exemplary aggregated data.Distribution data 440 may be used to update an RUL model, as will bedescribed in more detail below. The server system 401 may be configuredto modify the distribution data 440 as new data is aggregated, and ifthe distribution data 440 indicates that an RUL model is inaccurate, theserver system 401 may update the corresponding RUL model. Changes indistribution data 440 may be represented in an updated RUL model 442.

The updated RUL model 442 may be distributed to the plurality ofvehicles 420 and/or development database 408 via the network 410. Itshould be appreciated that the distribution data 440 is visuallyrepresented to aid in clarity of discussion of the distribution data440, and that the distribution data 440 may take on other forms withoutdeparting from the scope of the disclosure. Further, while thedistribution data 440 and updated RUL model 442 are shown separatelyfrom the server system 401, it should be understood that thedistribution data 440 and updated RUL model 442 may be stored on theserver system 401, any of the databases disclosed herein (e.g.,databases 402, 404, 406, development database 408), and/or combinationsthereof.

Turning now to FIG. 5 , an example method 500 is shown for developingand updating RUL models to monitor vehicle component health usingfederated learning, where connected data availability is assumed.Connected data availability may include communication with a connectedvehicle population or a plurality of databases and/or servers using aV2V network, a V2I network, a cloud network, and the like. Instructionsfor carrying out at least part of method 500 may be stored on andexecuted by a cloud-based health monitoring system, such as serversystem 401 of FIG. 4 .

At 502, method 500 includes collecting vehicle data from a plurality ofvehicles of the connected vehicle population. The vehicle data may beused to establish distinct vehicle classes and may include historic dataand calibration data of the vehicle. For example, the vehicle data mayinclude manufacturer and/or vehicle taxonomy data (e.g., fromengineering database 402 of FIG. 4 ), such as make, model, powertrain,drive line, wheel base, and the like. The manufacturer data may includeengineering data and operating region conditions, which may furtherinclude ambient temperature, humidity, traffic patterns, altitude, andthe like. The vehicle data may include a size, shape, and/or dimensionsof the vehicle, or any other vehicle information.

At 504, method 500 includes partitioning the connected vehiclepopulation into a plurality of vehicle classes based on the vehicle dataand training class-specific RUL models for each vehicle class of the Nvehicle classes. Each vehicle class of the plurality of vehicle classesmay include vehicles with similar characteristics and/or components,where members of the vehicle class share the similar characteristicsand/or components and members of other vehicle classes do not share thecharacteristics and/or components.

Partitioning of the connected vehicle population into vehicle classestraining class-specific RUL models for each vehicle class may be carriedout in various ways. In some embodiments, the connected vehiclepopulation may be partitioned based on one or more clustering methods,as described in greater detail below in reference to FIG. 8 . In otherembodiments, the connected vehicle population may be partitioned basedon an unsupervised learning algorithm, as described in greater detailbelow in reference to FIG. 9 . As new vehicle components and newvehicles are added to the connected vehicle population, the connectedvehicle population may be repartitioned to add, remove, or adjustboundaries of the vehicle classes.

At 506, method 500 includes deploying local RUL models at each vehicleof the connected vehicle population. In some embodiments, the local RULmodels may be copies of a class-specific RUL model of an associatedvehicle class of the connected vehicle population, which may beinitialized with pre-deployment calibrations. The pre-deploymentcalibrations may be determined based on information compiled from knownuse cases and historical/statistical data associated with the vehiclecomponent of the local RUL model. For example, as described above inreference to FIG. 4 , the historical/statistical data may includeresearch and engineering data, manufacturer default data, warranty data,repair/dealership data, test data and test drive data, etc.

As an example, each vehicle of a first vehicle class may have a firstlocal RUL model corresponding to a specific fuel injector of a firsttype of the vehicle, and each vehicle of a second vehicle class may havea second local RUL model corresponding to a specific fuel injector of asecond type of the vehicle. The first local RUL model may be the same asor substantially similar to a first class-specific RUL model of thefirst vehicle class, and the second local RUL model may be the same asor substantially similar to a second class-specific RUL model of thesecond vehicle class. More specifically, the first local RUL model ofthe first vehicle class may be identical to the first class-specific RULmodel, but may include local parameters which may be updated locally,and the second local RUL model of the second vehicle class may beidentical to the second class-specific RUL model, but may include localparameters which may be updated locally. Thus, the first local RUL modelof the first vehicle may be occasionally or periodically modified basedon a performance of a fuel injector specific to the first vehicle (e.g.,of the first type of fuel injector), and the second local RUL model ofthe second vehicle may be occasionally or periodically modified based ona performance of a fuel injector specific to the second vehicle (e.g.,of the second type of fuel injector).

At 508, method 500 includes monitoring data streams of the connectedvehicle population for requests to initiate FL. Monitoring the datastreams of the connected vehicle population may include monitoringdetection of degradations of a plurality of different vehicle componentsover a total population of vehicles of the connected vehicle populationand/or subpopulations of vehicles within the total population ofvehicles, where each subpopulation corresponds to a vehicle class. Forexample, a vehicle system (e.g., an engine system, a fuel system, etc.)of a vehicle of the total population of vehicles may include a pluralityof components, where the plurality of components may be modeled by acorresponding plurality of local RUL models corresponding to the vehicleclass of the vehicle.

As the connected data streams are monitored for detection ofdegradations, degradation data of the component may be periodicallyreceived at various vehicles of the connected vehicle population and thevehicle classes within the connected vehicle population. The degradationdata may include information about a total degradation (e.g., a completefailure of the vehicle component), such as, for example, a date and timeof the total degradation, operating conditions at the time of the totaldegradation, and so forth. The degradation data may also include sensordata relating to a total or partial degradation. For example, sensorreadings of the vehicle at a time of a total degradation may becollected as contextual information with respect to the totaldegradation, or sensor readings of the vehicle component may becollected that indicate a partial degradation of the vehicle component.

In some examples, degradation data may be received when a driver is senta first notice of service (FNOS) and brings a vehicle in for servicing,and a vehicle component is inspected and/or replaced. A potentialdegradation indicated by one or more sensors of the vehicle may beverified (e.g., a true positive), or the degradation may not be verified(e.g., a false positive). In another example, the degradation data maybe received when a driver is notified and does not bring a vehicle infor servicing, and a total degradation of the component occurs in thefield. The total degradation of the component may occur when the RUL ofthe component is close to 0 (e.g., a true negative), or the totaldegradation of the component may occur when the RUL of the component isgreater than or less than 0 (e.g., a false negative). Thus, asdegradation data is aggregated, a performance of the local RUL modelsmay be evaluated. When the degradation data indicates that an accuracyof a local RUL model of a component is high, the local RUL model may notbe adjusted. When the degradation data indicates that an accuracy of thelocal RUL model is not high, the local RUL model may be adjusted.Processing the degradation data and updating the local RUL model isdescribed in greater detail below in reference to FIG. 6 .

When a degradation of a vehicle component occurs in a vehicle, or when athreshold amount of the degradation data is collected by the vehicle, acontroller of the vehicle may send a learning request (e.g., a requestfor class-based model parameters to be relearned) to the cloud-basedhealth monitoring system to update the class-specific RUL model of thecomponent associated with the vehicle class of the vehicle, based on thedegradation data collected at the vehicle. Updating of theclass-specific RUL model may be carried out via an FL strategy, asdescribed below.

At 510, method 500 includes determining whether a learning request hasbeen received. If at 510 it is determined that a learning request hasnot been received, method 500 proceeds back to 508 to continuemonitoring for learning requests. If at 510 it is determined that alearning request has been received, method 500 proceeds to 512.

At 512, method 500 includes executing an FL learning session for aselected vehicle class. Within a federated learning framework,parameters of locally trained models (e.g., the local RUL models) areaggregated by a centralized server (e.g., at the cloud-based healthmonitoring system) and sent back to the locally trained models asupdates, resulting in local models that are more accurate and that haveimproved generalization capabilities. In particular, FL may be used toisolate drift in models caused by variance in deterioration of a modeledcomponent across a vehicle population. Executing the FL learning sessionfor the selected vehicle class is described below in reference to FIG. 7.

At 514, method 500 includes updating a master RUL model of the vehiclecomponent. The master RUL of the vehicle component may be a master RULmodel applicable to the entire connected vehicle population. The masterRUL model may be used, for example, to inform a warranty policy of thevehicle component and/or vehicle, or to inform design changes in themanufacture of new vehicle components, or for other research and/ortesting purposes.

In some embodiments, parameters of the master RUL model may beestablished and/or updated based on parameters of the class-specific RULmodels, as described below in reference to FIG. 8 , while in otherembodiments, the master RUL model may be pre-trained on an initialtraining dataset including labeled data with ground truth degradationdata of the vehicle component from the entire connected vehiclepopulation. However, training of the master RUL model based on labeleddata from the entire connected vehicle population may result in poorperformance of the master RUL model, due to wide variation in vehiclecomponent performance across the connected vehicle population.Additionally, sufficient ground truth labeled data may not be availableand/or may be slow and/or difficult to accumulate. Ground truth datasuch as component degradation data may be received after long delays,for example, through dealerships or warranty claims. A part may bedeemed healthy until a degradation is detected, and a transition fromhealthy to degraded may be sudden and without advance notice. As aresult of an insufficiency of labeled ground truth data, training of themaster RUL model may be continued over time via an FL strategy. The FLstrategy used to train the master RUL model may be similar to the FLstrategy used to train the class-specific RUL models described in FIG. 7.

However, while the FL strategy used to train the class-specific RULmodels may generate learning federations from a population of vehiclesof a vehicle class, the FL strategy used to train the master RUL modelmay include creating one or more learning federations from vehicles ofthe connected vehicle population in accordance with one or more samplingstrategies. In some embodiments, the one or more sampling strategies mayinclude a stratified sampling strategy, where samples are drawn fromhomogeneous groups of vehicles of the connected vehicle population; or acluster sampling strategy, where samples are drawn from heterogeneousclusters of the vehicles of the connected vehicle population; or a mixedsampling strategy, where samples are drawn from heterogeneous clustersof a homogeneous group of the vehicles of the connected vehiclepopulation. Further, a plurality of FL sessions may be carried out on arespective plurality of different learning federations.

For example, the master RUL model may be trained in a first FL sessioncarried out on a first learning federation drawn from a homogeneousgroup of vehicles of the connected vehicle population (e.g., via thestratified sampling strategy), such as vehicles of a particular make andmodel operating in a first geographic location with high humidity. Themaster RUL model may be trained in a second FL session carried out on asecond learning federation drawn from a heterogeneous group of vehiclesof the connected vehicle population (e.g., via the clustering samplingstrategy). The heterogeneous group of vehicles may comprise, forexample, a random selection of vehicles of the connected vehiclepopulation which may not have similar features. The master RUL model maybe trained in a third FL session carried out on a third learningfederation drawn from a combination of homogenous and heterogeneousgroups of vehicles of the connected vehicle population (e.g., via themixed sampling strategy), where vehicles of the third learningfederation may include vehicles of a specific make and model from aspecific geographic location, with a first portion of sample vehiclesdrawn from a sub-population of aggressively driven vehicles and a secondportion of sample vehicles drawn from a sub-population of cautiouslydriven vehicles. In some embodiments, the first, second, and third FLsessions may be carried out in order, while in other embodiments, thesecond and/or third FL sessions may be carried out before the first FLsession, or in a different order.

By training the master RUL model on a combination of homogeneous groups(where model accuracy may be increased for the homogeneous group) andheterogeneous groups (where model accuracy may be increased acrosshomogeneous groups), the master RUL model may be optimized based ondifferent interests of a manufacturer of the vehicle component. Forexample, in a first embodiment, the manufacturer may discover thatdegradations of the vehicle component occur at a much higher rate at afirst location than a second location, and may therefore wish tooptimize the master RUL model for accuracy on vehicles of the firstlocation.

In a second embodiment, the master RUL model may be optimized for anoverall accuracy over the entire connected vehicle population, wheretraining the master RUL model may include averaging parameters of themaster RUL model across a plurality of learning federations. Forexample, a first FL session with a first learning federation may resultin a first parameter adjustment of the master RUL model, a second FLsession with a second learning federation may result in a secondparameter adjustment of the master RUL model, and a third FL sessionwith a third learning federation may result in a third parameteradjustment of the master RUL model. Parameters of the master RUL modelmay then be updated by averaging the first parameter adjustment, thesecond parameter adjustment, and the third parameter adjustment, andapplying a resulting average parameter adjustment to the parameters ofthe master RUL model.

In other embodiments, a weighted average parameter adjustment may beapplied to the parameters of the master RUL model. For example, anaccuracy of the master RUL model may be optimized for the entireconnected vehicle population by multiplying one or more of the firstparameter adjustment, the second parameter adjustment, and the thirdparameter adjustment by a corresponding weight value prior to averagingthe first parameter adjustment, the second parameter adjustment, and thethird parameter adjustment. The weighting of various parameteradjustments may depend on one or more operational or developmental goalsof the manufacturer and/or a vehicle fleet manager.

Turning now to FIG. 6 , an example method 600 is shown for initiating anFL session of a class-specific RUL model by sending a learning requestof an RUL model of a component of a vehicle when connected vehicle datais available. The class-specific RUL model may be associated with aplurality of vehicles belonging to a vehicle class of a connectedvehicle population. Instructions for carrying out method 600 may beexecuted by a controller of the vehicle, such as controller 12 ofcontrol system 14 of FIG. 1 and/or the control system 290 of FIG. 2 . Inone example, method 600 may be executed as part of method 500 describedabove.

At 602, method 600 includes estimating and/or measuring vehicleoperating conditions. For example, the vehicle operating conditions mayinclude, but are not limited to, a status of an engine of the vehicle(e.g., whether the engine is switched on), and an engagement of one ormore gears of a transmission of the vehicle (e.g., whether the vehicleis moving). Vehicle operating conditions may include engine speed andload, vehicle speed, transmission oil temperature, exhaust gas flowrate, mass air flow rate, coolant temperature, coolant flow rate, engineoil pressures (e.g., oil gallery pressures), operating modes of one ormore intake valves and/or exhaust valves, electric motor speed, batterycharge, engine torque output, vehicle wheel torque, etc. In one example,the vehicle is a hybrid electric vehicle, and estimating and/ormeasuring vehicle operating conditions includes determining whether thevehicle is being powered by an engine or an electric motor. Estimatingand/or measuring vehicle operating conditions may further includedetermining a state of a fuel system of the vehicle, such as a level offuel in the fuel tank, determining a state of one or more valves of thefuel system, etc.

At 604, method 600 includes monitoring a health of vehicle components ofthe vehicle. Monitoring the health of the vehicle components may includemonitoring a status of each vehicle component of the vehicle componentsto determine if there is a degradation of the vehicle component. Thedegradation may be a total degradation, where the vehicle componentloses function in the field, or the degradation may be a partialdegradation, where the component may still function, but at a lowerfunctionality such that a performance of the vehicle is impacted. Thetotal degradation or the lower functionality may be detected by one ormore sensors of the vehicle (e.g., the sensors 16 of FIG. 1 ). Forexample, a sensor of the one or more sensors may output a performance ofthe vehicle component, where if the performance of the vehicle componentdecreases it may be determined that the vehicle component is degraded.In other scenarios, the total degradation or the lower functionality ofthe vehicle component may be detected by sensors coupled to othercomponents of the vehicle, where if a performance of the othercomponents of the vehicle decrease, it may be determined that thevehicle component is degraded.

At 606, monitoring the health of the vehicle components further includesreceiving data of class-specific RUL models of the vehicle componentsfrom a cloud-based server, such as the cloud-based server system 401 ofFIG. 4 . In various embodiments, the data may be sent from thecloud-based server during an FL session initiated as a result of adegraded vehicle component at a different vehicle of the vehicle class,as described in greater detail below in reference to FIG. 7 . The datamay include parameters of a class-specific RUL model of the degradedvehicle component, an updated class-based RUL of the degraded vehiclecomponent, and/or other information. The updated class-based RUL of thedegraded vehicle component may be used to adjust a time to send a firstnotice of service (FNOS) to a driver of the vehicle, as described ingreater detail below.

At 608, monitoring the health of the vehicle components of the vehiclefurther includes updating local RUL models of the vehicle components asclass-specific RUL model data is received. For example, a fuel injectorof a different vehicle may degrade in the field. As a result of the fuelinjector of the different vehicle degrading in the field, the differentvehicle may send a request to the cloud-based server to initiate an FLsession. During the FL session, the cloud-based server may update aclass-specific RUL model of the fuel injector. When the FL session ends,the cloud-based server may send data of the class-specific RUL model(e.g., the class-specific RUL model, parameters of the class-specificRUL model, an updated class-based RUL of the fuel injector, etc.) to thevehicle. When the data is received at the vehicle, the local RUL modelof the fuel injector of the vehicle may be updated based on the data. Insome embodiments, the local RUL model may be updated by calculating aweighted average of parameters of the local RUL model and parameters ofthe class-specific RUL model.

At 610, monitoring the health of the vehicle components further includesupdating predicted RULs of the vehicle components. Updating thepredicted RULs may include, for example, periodically updating local RULmodels with new data and events (e.g. failure or no failure, technicianchecks, or other reference signals, if any) and/or adjusting thepredicted RULs based on a predicted RUL of a class-based RUL model.

At 612, method 600 includes determining whether a degradation of avehicle component has been detected. If at 612 it is determined that nodegradation is detected, method 600 proceeds to 614.

At 614, method 600 includes determining whether the RUL of the componentis equal to the minimum RUL of the component, where the minimum RUL ofthe component may indicate a predicted end of a lifetime of thecomponent. For example, the detected degradation may occur prior to theRUL achieving a minimum RUL of the component, where the degradation isan unexpected early fault of the component. The detected degradation mayoccur at the minimum RUL of the component, where the degradation isexpected in accordance with a relevant RUL model. The detecteddegradation may occur after achieving the minimum RUL of the component,where a life of the component is longer than predicted by the relevantRUL model.

Referring briefly to FIG. 11 , in an embodiment, a graph 1100 of ahealth index of a component of a vehicle over time is shown. In otherembodiments, graph 1100 may show the health index of the vehiclecomponent over a mileage driven by the vehicle during a lifetime of thecomponent, or a number of relevant cycles, or a different parameter thatmeasures a use of the component. In graph 1100, a health index plot 1102shows a gradual decrease in a predicted RUL of the component from apoint 1106 where the component is first deployed as now, to a pointwhere it reaches an end of a lifetime of the component. A minimum RUL ofthe component is shown by dashed line 1104. An expected lifetime of thecomponent is shown by line 1110, where a degradation occurring at point1114 (e.g., at time 2) occurs where plot 1102 reaches the minimum RULshown by dashed line 1104. If a degradation is detected at point 1114,an accuracy of the RUL model may be considered to be high.Alternatively, a degradation may be detected prior to the componentreaching the minimum RUL, for example, at point 1108 at time 1. If adegradation is detected at point 1108, an accuracy of the RUL model maybe considered to be lower than desired. In another example, adegradation may be detected after the component reaches the minimum RUL,as indicated by line 1112. For example, if a degradation is detected atpoint 1116, an accuracy of the RUL model may be considered to be lowerthan desired. In some embodiments where a plurality of RUL models may beassociated with the component, when the accuracy of the RUL model isconsidered lower than desired, a different RUL model predicting adifferent RUL may be selected as a top-performing model for the purposesof predicting the RUL of the component, and local RUL models may beupdated in accordance with the different RUL model.

Returning to FIG. 6 , if at 614 it is determined that the RUL of thecomponent is not equal to the minimum RUL of the component, method 600proceeds back to 604. Alternatively, if at 614 it is determined that theRUL of the component is equal to the minimum RUL of the component,method 600 proceeds to 616. At 616, method 600 includes issuing a firstnotice of service (FNOS) to a driver of the vehicle to have the vehiclebrought in to have the component serviced, and method 600 proceeds backto 604.

Referring back to 612, if at 612 it is determined that a degradation ofthe component has been detected, method 600 proceeds to 618. At 618,method 600 includes an FNOS to the driver of the vehicle to have thevehicle brought in to have the component serviced.

At 620, method 600 includes determining whether an RUL of the componentis equal to a minimum RUL of the component, as described above inreference to 614. If at 620 it is determined that the RUL of thecomponent is equal to the minimum RUL, method 600 proceeds back to 604,where monitoring the health of the vehicle components is continued. Ifat 620 it is determined that the RUL of the component is not equal tothe minimum RUL (e.g., either the component has degraded unexpectedlyearly, or unexpectedly late), method 600 proceeds to 622.

At 622, method 600 includes storing data of the degradation data locallyand updating one or more local RUL models of the component at thevehicle, based on the degradation data. The type of degradation data maydepend on the component. As an example, the degradation data may includea time of the degradation, a total lifetime of the component up untilthe time of the degradation in time, a mileage of the vehicle, atemperature of exhaust gases of the vehicle, a crankshaft velocity ofthe vehicle, a speed of the vehicle, a fluctuation in engine torque ofthe vehicle, a state of charge of a battery of the vehicle, and thelike.

The one or more local RUL models of the component may be updated withnew data (the new data forming a mini batch of training data), andsubsequently optimized in accordance with an iterative optimizationprocedure. The iterative optimization procedure may use a gradientdescent algorithm starting at a point β_(m) (learned from each class),to adjust the parameters of a model of the one or more local RUL modelsbased on a gradient of the function with individual historical data, inaccordance with the following equation:

β_(m) ^(new)=β_(m) ^(old)−η∇_(β) _(m) J _(m)(β_(m) ;s _(m))  (1)

where a loss function J_(m) is an error between the model and the truesystem behavior for the mth class. The parameters β_(m) may be updatedin an opposite direction of the gradient of the loss function withrespect to ∇_(β) _(m) J_(m). The step size η is an importanthyperparameter that determines how fast to converge to a (local)minimum.

In addition to the one or more local RUL models, in some embodiments oneor more AD models of the component may be updated locally (e.g., at thevehicle) with the degradation data. In some embodiments, a cloud-basedhealth monitoring system may also be notified, whereby one or morecloud-based AD models may be updated, and/or the degradation data may berelayed to a manufacturer of the component

At 624, method 600 includes determining whether the RUL of the componentis greater than the minimum RUL of the component (e.g., where thecomponent has degraded earlier than expected). If the RUL of thecomponent is not greater than the minimum RUL of the component (e.g., ifthe RUL of the component is less than the minimum RUL of the component,meaning that the component has lasted longer than expected), method 600proceeds back to 604, where monitoring the health of the vehiclecomponents is continued. If the RUL of the component is greater than theminimum RUL of the component, method 600 proceeds to 626.

At 626, method 600 includes sending a request to a cloud-based server toinitiate an RUL learning cycle using federated learning. The RULlearning cycle using federated learning is described below in referenceto FIG. 7 .

Turning now to FIG. 7 , an exemplary method 700 is shown for updating atrained RUL model of a component of a vehicle via federated learning. Invarious embodiments, the centralized server may be a cloud-based server,such as server system 401 of FIG. 4 . Instructions for carrying outmethod 700 may be stored on and executed by the cloud-based server. Inone example, method 700 is executed as part of method 500 describedabove.

At 702, method 700 includes receiving a request to initiate a federatedlearning cycle for the component. The request may be received from arequesting vehicle as a result of a degradation being detected in thecomponent at the requesting vehicle, as described above in reference toFIG. 5 .

At 704, method 700 includes creating a learning federation (alsoreferred to herein as the federation) of vehicles from the connectedvehicle population. In various embodiments, the federation may comprisea number N of randomly selected vehicles of a vehicle class as therequesting vehicle. For example, 100 vehicles of the connected vehiclepopulation may be randomly selected to create the federation. In someembodiments the federation may include the requesting vehicle, while inother embodiments, the federation may not include the requestingvehicle.

At 706, method 700 includes requesting model parameters from local RULmodels of the federation of vehicles. The model parameters may include,for example, model inputs, model outputs, and model error; weights andbiases of neural network RUL models; and/or other parameters. In someembodiments, the model parameters may include sensor and event data ofthe vehicle, as measured via vehicles sensors such as the sensors 16described above in reference to FIG. 1 . The sensor and event data mayinclude degradation data, as described above in reference to FIG. 6 , ordiagnostic trouble codes (DTC), which may be triggered by one or morediagnostic routines executed by the controller, or other data. In someembodiments, the sensor and event data may be used to establish labeled,ground truth information for supervised learning (SL) or self-supervisedlearning.

Method 700 may include waiting for a duration to receive responses fromthe vehicles of the federation. In some cases, a portion of the vehiclesof the federation may not respond and/or transmit the model parametersand errors, whereby non-responding vehicles may be eliminated from thefederation. In some embodiments, eliminated vehicles may be replaced byother vehicles of the vehicle class.

At 708, method 700 includes aggregating the model parameters and themodel errors of the local RUL models from the vehicles of thefederation, and updating a class-specific RUL model of the vehicle classat the cloud-based server. Updating the class-specific RUL model maydepend on a type of model used. For example, if the class-specific RULmodel is a neural network, a plurality of weights of the neural networkmay be adjusted based on new degradation data. If the class-specific RULmodel is a regression model, the regression model may be adjusted toinclude new data points corresponding to the new degradation data.

In some embodiments, aggregating the model parameters of the local RULmodels to update the class-specific RUL model may include averaging themodel parameters of the local RUL models. The averaging may also be aweighted averaging, where model parameters of certain local RUL modelsmay be weighted more than other local RUL models (e.g., where somevehicle components exhibit behavior that is more representative of thevehicle class than other vehicle components). In some embodiments,parameters of the class-specific RUL models may be updated as a functionof the aggregated and/or averaged local RUL model parameters, while inother embodiments, the parameters of the class-specific RUL models maybe replaced by the averaged local RUL model parameters.

At 710, method 700 includes wirelessly transmitting updated modelparameters of the class-specific RUL model to vehicles of the federation(e.g., the N randomly selected vehicles of the vehicle class). When theupdated model parameters are received at the vehicles of the federation,each vehicle of the federation may update a corresponding local RULmodel based on the updated model parameters. In some embodiments, thelocal RUL model parameters of each vehicle of the federation may bereplaced with the updated model parameters. In other embodiments, thelocal RUL model parameters of each vehicle of the federation may beupdated as a function of the updated model parameters (e.g., averaged).

Once the updated model parameters are transmitted to the applicablevehicles, a first federated learning cycle is completed, and method 700includes determining whether to initiate a subsequent federated learningcycle. Determining whether to initiate a subsequent federated learningcycle may depend on factors including a number of federated learningcycles performed and a convergence of the model parameters over aplurality of federated learning cycles.

At 712, method 700 includes determining whether more than one federatedlearning cycle has been completed. If only one federated learning cyclehas been completed, sufficient data may not be available to establish aconvergence of the model parameters. To establish a convergence of themodel parameters, at least two federated learning cycles may becompleted. If at 712 it is determined that only one federated learningcycle has been completed, method 700 may proceed back to 704, where anew federation is created from the connected vehicle population and asubsequent FL cycle is performed. If at 712 it is determined that morethan one FL cycle has been completed, method 700 proceeds to 714.

At 714, method 700 includes determining whether a new FL session hasbeen requested. For example, a component of a first vehicle mayexperience a first degradation, where in response to the firstdegradation, a first request is sent to initiate an FL session. Inresponse to receiving the first request from the first vehicle, a firstFL session may be initiated in accordance with method 700. During thefirst FL session, a similar component of a second vehicle may experiencea second degradation, where in response to the second degradation, asecond request is sent to initiate an FL session. In response toreceiving the second request, the first FL session still underway, asecond FL session may be initiated, where the second FL session includesdegradation data of the second degradation as well as model data of thesecond vehicle and/or other similar vehicles. Since the second FLsession is based on additional and/or more recent model data than thefirst FL session, in some embodiments, the first FL session may beabandoned. In other embodiments, the first FL session may be carried outto a completion (e.g., a convergence of model data) concurrently withcarrying out the second FL session.

If at 714 it is determined that a learning session has been requested,method 700 proceeds back to 704, where a new federation is created fromthe connected vehicle population and a subsequent federated learningcycle is performed based on updated model parameters included in therequest for the new federated learning session. If at 714 it isdetermined that a learning session has not been requested, method 700proceeds to 716.

At 716, method 700 includes determining whether the model parametershave converged. Determining whether the model parameters have convergedmay include determining whether an aggregate change made to the modelparameters in accordance with the gradient descent ∇_(β) _(m)J_(m)(β_(m);s_(m)) from equation (1) has decreased below a thresholdvalue (e.g., a small positive value close to zero).

For example, the connected vehicle population may comprise 1000vehicles. A federated learning session may be initiated in relation to avehicle component, starting with a first federated learning cycle basedon a first randomly selected federation of 100 vehicles. During thefirst federated learning cycle, parameters of local models of thevehicle component at the first randomly selected federation of 100vehicles may be transmitted to a server, and aggregated at the server toperform a first update of a class-specific model at the server.Parameters of the class-specific model may be transmitted back to thefirst randomly selected federation of 100 vehicles, and the local modelsof the vehicle component at the first randomly selected federation of100 vehicles may be updated. During updating of the local models,parameters of the local models may be reset to parameters of theclass-specific model and retrained based on local (e.g., historical)data. After updating the local models, the first federated learningcycle may conclude, and a second federated learning cycle of thefederated learning session may be initiated based on a second randomlyselected federation of 100 vehicles of the 1000 vehicles of theconnected vehicle population. During the second federated learningcycle, parameters of local models of the vehicle component at the secondrandomly selected federation of 100 vehicles may be transmitted to theserver, and aggregated at the server to perform a second update of themaster model. Updated parameters of the master model may be transmittedback to the second randomly selected federation of 100 vehicles, and thelocal models of the vehicle component at the second randomly selectedfederation of 100 vehicles may be updated. After updating the localmodels, the second federated learning cycle may conclude, and a thirdfederated learning cycle of the federated learning session may beinitiated based on a third randomly selected federation of 100 vehiclesof the 1000 vehicles of the connected vehicle population, and so on. Assuccessive federated learning cycles are performed, adjustments made tothe parameters of the master model may decrease. When the adjustmentsmade to the parameters of the master model, in aggregate, decrease belowthe threshold value, the model parameters may be considered to haveconverged. Convergence may also be affected by new requests forfederated learning, whereby new model and degradation data may beperiodically or continuously received.

At 718, method 700 includes sending updated class-specific RUL modeldata to all vehicles of the vehicle class of the class-specific RULmodel. Over the course of the FL session, parameters of theclass-specific RUL model are updated based on parameters of local modelsof successive randomly selected federations. After a convergence occursand the FL session ends, a set of final parameters of the class-specificRUL model may be sent to all vehicles of the vehicle class, includingvehicles included in one or more randomly selected federations. In thisway, all local RUL models of the vehicles of the vehicle class mayreceive, be updated, and be maintained with a most recent and accurateset of class-specific RUL model parameters. The class-specific RUL modeldata may include, for example, the class-specific RUL model, parametersof the class-specific RUL model, a class-based predicted RUL of thevehicle component, or other relevant information.

Referring now to FIG. 8 , a first exemplary method 800 for partitioninga vehicle population into a plurality of vehicle classes is shown. Asdescribed above, by maintaining class-specific RUL models of a componentfor each vehicle class rather than relying on a master RUL model for thecomponent across all vehicle classes, a performance of a specificcomponent of a vehicle may be modeled more accurately. Instructions forcarrying out method 800 may be stored on and executed at a cloud-basedserver, such as the server system 401 of FIG. 4 . Method 800 may beexecuted as part of method 500 described above.

In various embodiments, the plurality of vehicle classes may be specificto a vehicle component. For example, the vehicle population may bedivided into a first set of vehicle classes with respect to a firstcomponent; a second set of vehicle classes with respect to a secondcomponent; a third set of vehicle classes with respect to a thirdcomponent, and so on. By creating different sets of vehicle classes fordifferent components, local and master RUL models of a component may bemore accurate. In other embodiments, the vehicle population may bedivided into a single set of vehicle classes, where vehicle classes maynot be specific to a vehicle component. For example, the vehicle classesmay be established based on features specific to a vehicle's geometriclocation and/or a historical driving profile of the vehicle, as opposedto a vehicle component.

At 802, method 800 includes selecting a set of features to be used forestablishing vehicle classes with respect to a modeled vehiclecomponent. The set of features may be selected from vehicle data, ascollected and described above in reference to FIG. 5 . However, not allthe vehicle data collected from the connected vehicle population may berelevant to classification of a vehicle component. For example, whenestablishing vehicle classes with respect to a first vehicle component,a first sensor data relevant to the first vehicle component may beselected. When establishing vehicle classes with respect to a secondvehicle component, a second sensor data relevant to the second vehiclecomponent may be selected, where portions or all of the second sensordata and the first sensor data may be different.

Some features of the vehicle data may be relevant to all vehicle classesof all components. For example, a geographical location of a vehicle maybe a feature considered in establishing all vehicle classes. As anotherexample, in some embodiments, a model of a vehicle may be relevant toestablishing vehicle classes for all components, where one definingfeature of a vehicle class of a component may be a model of a vehicle.In other embodiments, the model of the vehicle may not be relevant toestablishing vehicle classes for all components. For example, for somecomponents, a type of vehicle may be more useful in establishing vehicleclasses than the model of the vehicle. For example, a sedan of a firstmodel may share features with a sedan of a second model, whereby thesedan of the first model and the sedan of the second model may beincluded in a same vehicle class.

Once a set of features has been selected as described above, parametervectors may be established for each vehicle including the vehiclecomponent. The parameter vectors may include a number of valuescorresponding to a size of the set of features, where each value of thenumber of values corresponds to a feature of the set of features. Thus,a parameter vector of a vehicle may represent the vehicle, and adistance between a first parameter vector of a first vehicle and asecond parameter vector of a second vehicle may represent a similarityof the first vehicle to the second vehicle. If the first vehicle issimilar to the second vehicle, the first vehicle and the second vehiclemay be assigned to a same vehicle class of the connected vehiclepopulation with respect to the vehicle component. Alternatively, if thefirst vehicle is not similar to the second vehicle, the first vehicleand the second vehicle may be assigned to different vehicle classes ofthe connected vehicle population with respect to the vehicle component.

At 804, method 800 includes using one or more clustering methods todivide the vehicle population into partitions (e.g., vehicle classes)based on the selected set of features and corresponding parametervectors. The one or more clustering methods may use a similarity metric,as described above, to determine how the parameter vectors of vehiclesof the vehicle population may be divided into natural groupings. In someembodiments, one or more algorithms of the one or more clusteringmethods may be adjusted to reflect a relative importance of a parameterof the parameter vectors. For example, if an output of a first sensorhas a higher importance in modeling a vehicle component than an outputof a second sensor, a weight may be multiplied by a value of theparameter. In other embodiments, the relative importance of a parameterof the parameter vectors may be indicated in a different way.

Various clustering methods may be used to cluster the vehicles of thevehicle population into partitions or classes, including but not limitedto K-means, self-organizing maps, k-nearest neighbors (KNN),density-based spatial clustering of applications with noise (DBSCAN),Gaussian Mixture Modeling (GMM), distance metrics, and so on. In someembodiments, a clustering method of the various clustering methods maybe used to determine a suitable number of vehicle classes to divide thevehicle population into, while in other embodiments, the clusteringmethod may divide the vehicle population into a predetermined number ofvehicle classes. The number of vehicle classes may also be determined bythe clustering method itself, or statistical testing methods.Additionally, in some embodiments, after a first clustering operation isperformed, a second clustering operation may be performed. For example,a set of vehicle classes created as a result of applying a firstclustering operation may be further clustered (e.g., divided intoadditional vehicle classes) based on a geographical location of vehiclesof the vehicle classes, or a type of operation (e.g., aggressive drivingvs. cautious driving) of vehicles of the vehicle classes, or a differentcriterion.

In some embodiments, prior to clustering the vehicles, a dimensionalityreduction may be applied to the set of features to reduce a total numberof features used to establish vehicle classes. In other words, prior toclustering the parameter vectors, a dimensionality of the parametervector may be reduced, whereby a lesser number of parameters may beincluded in the parameter vector. For example, a set of features mayinclude 2000 features, from which 25 dominant features are selected viathe dimensionality reduction. Reducing the number of parameters in theparameter vector may reduce a probability of modeling errors due tooverfitting. Dimensionality reduction techniques may include principalcomponent analysis (PCA), variational autoencoders (VAE), and the like.

At 806, method 800 includes learning a distribution for each partition.Each vehicle may be assigned a partition (e.g., vehicle class), andpopulation-wide and partition-wide statistics may be collected, such asnumber of vehicles per partition, a population density of eachpartition, an average distance between vehicles in each partition andstandard deviation, etc. Prior to a deployment of a relevant vehiclecomponent and/or vehicle, vehicles of a vehicle class may belong to asame distribution. However, as vehicle components and data generatingsystems/subsystems may age differently, based on usage, over time thevehicles of the vehicle class may not belong to the same distribution.As a result, a local model of a vehicle component may drift with respectto a class-specific model of the vehicle class. One way of addressingthe drift is to periodically update the local model based on parametersof the class-specific model, as described above in reference to method500 of FIG. 5 . Another way to address the drift is to periodicallyre-partition the vehicle population into vehicle classes as degradationand/or other vehicle data is collected.

At 808, method 800 includes, for each modeled component of the vehiclepopulation, training a class-specific model (e.g., an RUL model) of thecomponent for each vehicle class to learn a set of model parameters,using a previously defined, master model for all vehicles of the vehiclepopulation. For example, a master RUL model may be associated with afuel injector used by some vehicles of the vehicle population, and notused by other vehicles of the vehicle population. The master RUL modelfor the vehicle population may be used to create a plurality ofclass-specific RUL models, where each class-specific RUL model may beassociated with a vehicle class that includes vehicles with the fuelinjector.

For example, vehicles of a first vehicle class may include a first typeof fuel injector, while vehicles of a second vehicle class may include asecond type of fuel injector. The first vehicle class may have a firstclass-specific RUL model associated with the first type of fuelinjector, and the second vehicle class may have a second class-specificRUL model associated with the second type of fuel injector. The firstclass-specific RUL model and the second class-specific RUL model may bestored in a cloud-based server, such as server system 401 of FIG. 4 .

Referring briefly to FIG. 10A, a plot 1000 is shown depicting an exampledistribution of vehicles 1010 within four vehicle classes of a vehiclepopulation. A first vehicle class 1002 may include a firstclass-specific RUL model 1014 for a vehicle component; a second vehicleclass 1004 may include a second class-specific RUL model 1016 for thevehicle component; a third vehicle class 1006 may include a thirdclass-specific RUL model 1018 for the vehicle component; and a fourthvehicle class 1008 may include a fourth class-specific RUL model 1020for the vehicle component. The first vehicle class 1002, the secondvehicle class 1004, the third vehicle class 1006, and the fourth vehicleclass 1008 may be separated by one or more boundaries 1012.

Returning to FIG. 8 , in some embodiments, different types ofclass-specific models may be used for different vehicle components. Forexample, a first model type may be used to model a first component, asecond model type may be used to model a second component, a third modeltype may be used to model a third component, and so forth. Additionally,in some embodiments, a plurality of RUL models may be associated with avehicle component, and a top-performing model may be selected and used.By generating the plurality of RUL models for each vehicle component, amodel best suited for predicting an RUL of the vehicle component may beidentified after the models are deployed.

In some embodiments, the plurality of RUL models may be periodicallyre-ranked based on a ranking algorithm and a new top-performing model ofthe plurality of RUL models may be selected. For example, a firsttop-performing RUL model may be selected prior to deployment, based onpre-deployment data; after a first time period after deployment, asecond top-performing RUL model may be selected, based on a first batchof degradation data of the component received after deployment; after asecond time period after deployment, a third top-performing RUL modelmay be selected, based on a second batch of degradation data of thecomponent received after deployment; and so on. When a new RUL model isselected, the master RUL model, the class-specific models, and the localmodels may be updated to the new RUL model.

The class-specific RUL models may be trained using a class-specifictraining dataset with labeled data including ground truth information.The labeled data may be compiled by a manufacturer of the vehiclecomponent associated with the class-specific RUL model, and may include,for example, engineering data (e.g., from engineering database 402 ofFIG. 4 ), component testing data, and/or other data collected during adevelopment of the vehicle component. The labeled data may also includedegradation data received from vehicles of the connected vehiclepopulation. For example, if a fuel injector of a vehicle degrades in thefield, information about the degradation, the fuel injector, thevehicle, operating conditions of the vehicle, geographical location ofthe vehicle, and so forth may be transmitted to the cloud-based healthmonitoring system, where the information may be included in the labeleddata for subsequent training purposes. However, because obtainingsufficient ground truth information may be difficult, after training onthe labelled data, parameters of the class-specific RUL models may befurther adjusted during subsequent training under an FL strategy, asdescribed in greater detail in reference to FIG. 7 .

Each of the class-specific RUL models for a given component may learndifferent parameters based on a corresponding class-specific trainingset. Specifically, a general RUL model may have the form:

ŷ(t)=ƒ(s(t),β)  (2)

where y may represent an RUL. The function ƒ may map input vehiclesamples s(t) to a model output y(t) at time t (t=1, . . . T). Thefunction ƒ is parameterized by β, which may be determined by minimizinga loss function defined by an error between a true state y and anestimated state ŷ=ƒ(s(t),β):

J=y−ƒ(s(t),β)  (3)

For a fleet (population) of vehicles divided into M classes, to obtainoptimal parameters for class models for the m^(th) (m=1, . . . M)vehicle class, one can write an optimization problem that minimizes theweighted sum of the loss function J_(m) as shown below:

$\begin{matrix}{J_{m}^{*} = {\min\limits_{\beta_{m}}\frac{1}{T}{\sum\limits_{t = 1}^{T}{{w_{m}\left( {s(t)} \right)}{J_{m}\left( {\beta_{m};{s_{m}(t)}} \right)}}}}} & (4)\end{matrix}$

where β_(m)=[β_(m1), . . . , β_(mp)] are parameters for theclass-specific models in the particular vehicle class, s_(m) aretraining samples from the mth class, sampled at time t (t=1, . . . , T),and the loss function J_(m) is the error between the current class-levelmodel and the true system behavior for the mth class. A weightingfunction w_(m) is a vector defined as:

$\begin{matrix}{{{w_{m}\left( {s(t)} \right)} = {\exp\left( \frac{- {{dis}\left( {m,{b(t)}} \right)}^{2}}{2\sigma^{2}} \right)}},{di{s\left( {.{,.}} \right)}}} & (5)\end{matrix}$

which is a topological distance between two vehicle classes, which canbe computed using a breadth-first procedure. The weighting function maysmoothen discontinuities along boundaries of adjacent regions.

In turn, local RUL models may be trained iteratively via a mini-batchbatch gradient descent starting from a point β_(m) (learned from eachclass), where parameters are adjusted based on a function gradient withindividual historical data:

β_(m) ^(new)=β_(m) ^(old)−η∇_(β) _(m) J _(m)(β_(m) ;s _(m))  (6)

where the parameters β_(m) are updated in an opposite direction of thegradient of the loss function with respect to ∇_(β) _(m) J_(m). A stepsize η may be a hyperparameter that dictates how fast convergence to a(local) minimum takes. A larger step size is likely to lead to aconvergence faster, but may overshoot with unstable results. A smallerstep size, on the other hand, may lead to very slow convergence. Acommon strategy to define the step size is to set it as a decreasingfunction of the number of updates.

A master RUL model F(t) for a vehicle component may be a summation ofeach class-specific RUL model of each vehicle class C_(m), as describedby equations (7) and (8) below:

$\begin{matrix}{{F(t)} = {\sum\limits_{m = 1}^{M}{{v_{m}\left( {s(t)} \right)}{f_{m}\left( {s(t)} \right)}}}} & (7)\end{matrix}$ $\begin{matrix}{{c_{m}\left( {s(t)} \right)} = \left\{ \begin{matrix}1 & {{s(t)} \in C_{m}} \\0 & {otherwise}\end{matrix} \right.} & (8)\end{matrix}$

where m is a number of vehicle classes and C_(m), m=1, . . . , M is adisjoint partition of an operating space that contains the parametervectors s(t) sampled at time t for a particular model, t is a signalsampling time, and m is the m^(th) partition.

At 810, method 800 includes determining whether a class-specific RULmodel error is greater than a threshold RUL model error. If at 810 it isdetermined that the class-specific RUL model error is greater than thethreshold RUL model error, method 800 proceeds back to 804, where thevehicle population is repartitioned. As a result of a re-partitioning,one or more class boundaries of the vehicle classes may be adjustedbased on the clustering methods.

Alternatively, if at 810 it is determined that the class-specific RULmodel error is not greater than the threshold RUL model error, method800 proceeds to 812. At 812, method 800 includes maintaining theexisting vehicle classes, and method 800 ends.

Method 800 may be used to generate initial vehicle classes, but in someembodiments available ground truth degradation data may not besufficient to use method 800 to generate optimal vehicle classes. Adifferent, alternative procedure for generating vehicle classes thatdoes not rely on ground truth data is described below in reference toFIG. 9 .

FIG. 9 shows a second, alternative exemplary method 900 for partitioninga vehicle population into a plurality of vehicle classes using growingself-organizing maps (GSOM). By employing a GSOM algorithm, vehicleclasses may be created based on prototypical high density parametervectors that may be dynamically adjusted as new vehicles are added tothe vehicle population and new data is received from vehicles of thevehicle population. Instructions for carrying out method 900 may bestored on and executed at a cloud-based server, such as the serversystem 401 of FIG. 4 . Method 900 may be executed as part of method 500described above.

Method 900 begins at 902, where method 900 includes dividing the vehiclepopulation into a small network of connected vehicle classes based on asimilarity metric, where each connected vehicle class is considered anode on the network. The vehicle classes may be specific to a vehiclecomponent, where different vehicle classes are created for differentcomponents. As described above in reference to method 800, each vehicleof the vehicle population may be represented as a vector of vehicleparameters (e.g., vehicle data collected as part of method 500 above),and the similarity metric may be applied to cluster vehicles intoinitial classes. For example, a degree of similarity between vehiclesmay be established by calculating a Euclidean distance between theparameter vectors of the vehicles. In some embodiments, a subset of thevehicles of the vehicle population may be used to generate an initialset of vehicle classes, and a size and number of vehicle classes may beadjusted over time as a remainder of the vehicles of the vehiclepopulation is incrementally added to the network of connected vehicleclasses.

At 904, method 900 includes assigning each vehicle class a definingparameter vector based on vehicle parameters for a master predictivemodel. In one example the defining parameter vector may be aprototypical parameter vector of the vehicles of the respective vehicleclass representing an average of the parameter vectors of the vehiclesbelonging to the vehicle class.

For example, the vehicle population may include a first vehicle classand a second vehicle class for a vehicle component. A first definingparameter vector of the first vehicle class may include a firstparameter indicating a first vehicle type; a second parameter indicatinga first vehicle year; a third parameter indicating a first vehicle size,and so on. A second defining parameter vector of the second vehicleclass may include a first parameter indicating a second vehicle type; asecond parameter indicating a second vehicle year; a third parameterindicating a second vehicle size, and so on. A vehicle with a parametervector that is more similar to the first defining parameter vector thanthe second defining parameter vector may be assigned to the firstvehicle class, while a vehicle with a parameter vector that is moresimilar to the second defining parameter vector than the first definingparameter vector may be assigned to the second vehicle class.

At 906, method 900 includes adding sample vehicles to the network ofconnected vehicle classes, where each sample vehicle is assigned avehicle class based on parameter vector similarity, in the mannerdescribed above. In other words, in a competitive learning stage,vehicle classes compete with each other for sample vehicles based on asimilarity and/or distance metric.

For example, a set of defining parameter vectors {ε_(m),m=1, . . . , M},which determine a center location of a respective set of vehicleclasses, may be adjusted with the recursive updating format:

ε_(m)(k+1)=ε_(m)(k)+ζ_(m)(k)h(k,dis(m,b))[s−ε _(m)], m=1, . . . ,M  (9)

where dis(.,.) is a topological distance between two vehicle classes,which can be computed using a breadth-first procedure. c is a BMU—anindex of the defining parameter vector closest to a training input s,while k is a number of updates of weight vectors over a fixed number ofvehicle classes. A maximum number for k is predefined to stop updatingof defining parameter vectors.

For each input s(t) at a given sampling time t, a corresponding BMU b(t)will be determined by:

$\begin{matrix}{{b(t)} = {\arg\min\limits_{m}{{{s(t)} - \varepsilon_{m}}}}} & (10)\end{matrix}$

A matching process of the input s and the BMU c indicates thecompetitive learning capability of the growing SOM method. Vehicleclasses compete with each other to win sampling data.

At 908, each time a sample vehicle is added to a class, method 900includes updating the parameters of the defining parameter vector basedon the parameters of the added sample vehicle (e.g., in a cooperativelearning stage). For example, if the defining parameter vector iscalculated based on an average of the parameter vectors of the vehiclesin the vehicle class, updating the parameters of the defining parametervector may include recalculating the average of the parameter vectors ofthe vehicles in the vehicle class including a new parameter vector ofthe added sample vehicle.

In some embodiments, cooperative learning may involve using aneighborhood function h (k,dis(m,b)) defined with a Gaussian kernel, as:

$\begin{matrix}{{h\left( {k,{di{s\left( {m,b} \right)}}} \right)} = {\exp\left( \frac{{- d}i{s\left( {m,b} \right)}^{2}}{2{\sigma^{2}(k)}} \right)}} & (11)\end{matrix}$

where σ is a non-increasing function of time defining an effective rangeof the neighborhood function. The use of the neighborhood functionactivates cooperative learning, enabling a defining parameter vector tobe updated with training data falling into the corresponding vehicleclass (e.g., Voronoi set), and also training data in neighboring vehicleclasses.

At 910, method 900 includes selecting a largest vehicle class, where thelargest vehicle class is the vehicle class with a highest class-levelmodel error (e.g., degree of divergence in a population distribution ofvehicles of the vehicle class). In other words, as new sample vehicledata is added to a vehicle class of the network, a populationdistribution of vehicles in the vehicle class may shift from a firstpopulation distribution to a second population distribution. The secondpopulation distribution may be substantially similar to the firstpopulation distribution, or the second population distribution may bedivergent with the first population distribution. A divergent populationdistribution may indicate an increasing heterogeneity of the vehicleclass, due to growing variances and/or shifting means within the vehicleclass.

Referring briefly to FIG. 10B, a divergence example diagram 1030 isshown including a first graph 1032 and a second graph 1034. The firstgraph 1032 shows a first population distribution plot 1036 of apredicted RUL of a vehicle component across a plurality of RUL models ofa respective plurality of vehicles of a vehicle class. The second graph1034 shows a second population distribution 1038 of a predicted RUL ofthe vehicle component across the plurality of RUL models of therespective plurality of vehicles of the vehicle class, superimposed onthe first population distribution plot 1036, where the second populationdistribution is a result of adding new sample data 1040 (e.g.,degradation data, new vehicle data, etc.) to the vehicle class. In thesecond graph 1034, a divergence 1042 can be seen. The divergence 1042may indicate a presence of two sub-populations of vehicles of thevehicle class. For example, the divergence 1042 may occur as a result ofan introduction of changes to a vehicle component by a manufacturer ofthe vehicle component, whereby vehicles including the vehicle componentwith the changes may have an RUL that is shorter than vehicles includingthe vehicle component without the changes. As a result of the occurrenceof the divergence 1042, the vehicle class may be divided into twovehicles classes (e.g., the vehicle class and a new vehicle class).Alternatively, if no divergence is detected in the first populationdistribution and the second population distribution, the vehicle classmay not be divided into two vehicles classes.

Returning to FIG. 9 , at 912, at the 900 includes determining whetherthe class-specific RUL model error (e.g., loss value) of the largestvehicle class is greater than a threshold loss value. If at 912 it isdetermined that the class-specific RUL model error of the largest classis greater than the threshold loss value, method 900 proceeds to 914.Alternatively, if at 912 it is determined that the class-specific RULmodel error of the largest class is not greater than a threshold lossvalue, method 900 proceeds to 916.

At 914, method 900 includes creating a new vehicle class (e.g., a newnode on the network) and dividing the vehicles of the largest classbetween the new vehicle class and the formerly largest vehicle class, inan adaptive learning stage. For example, vehicles with parameter vectorsmost similar to a defining parameter vector of the largest vehicle classmay remain in the formerly largest vehicle class, while vehicles withparameter vectors most similar to a defining parameter vector of the newvehicle class may be assigned to the new vehicle class.

For example, a penalty term ζ_(m)(k) may be determined by a value of alocal loss function J_(m) established for class-specific RUL modelparameter identification. The increment of ζ_(m) will lead to aparameter vector to move toward regions with higher loss functions.Thus, the GSOM network is grown based on the value of the loss functioninstead of visiting frequency or quantization errors. The penalty termis described as follows:

$\begin{matrix}{{\zeta_{m}(k)} = \frac{J_{m}(k)}{\sum_{m = 1}^{M}{J_{m}(k)}}} & (12)\end{matrix}$

If a largest ζ_(m) exceeds a predefined threshold, a new vehicle classmay be inserted between a vehicle class with the largest ζ_(m) and afurthest neighbor based on Euclidean distance. This will lead to a finerpartition of the vehicle class that cannot be sufficiently described bycurrent local model and requires further decomposition. Growth of theGSOM network may be terminated based on a first stopping criterion,where all ζ_(m), m=1, . . . , M are below the predefined threshold, or asecond stopping criterion, where a number of SOM nodes (number ofvehicle classes) exceeds a predefined number.

At 916, method 900 includes adjusting a location of each node of thenetwork with respect to topologically neighboring nodes (e.g.,neighboring vehicle classes) by updating the defining parameter vectorsof the vehicle classes in the network. As the defining parameter vectorsof the vehicle classes in the network are adjusted based on an additionof a new sample vehicle and/or a creation of a new node, An example ofthe process of creation of the new vehicle class and the division of thevehicles of the largest vehicle class into the new vehicle class and theformerly largest vehicle class is depicted in FIG. 10C.

Referring briefly to FIG. 10C, a sequence diagram 1060 shows five statesof a network at five different subsequent points in time during anapplication of a GSOM algorithm: a first state 1062, a second state1063, a third state 1064, a fourth state 1065, and a fifth state 1066.The network represents a vehicle population and includes five nodes,where each of the five nodes represents a vehicle class of the vehiclepopulation. In the first state 1062, each of the five nodes is depictedas having a different size, as each vehicle class may include adifferent number of vehicles.

In the second state 1063, a new sample vehicle 1070 is added to avehicle class of the network at a node 1072. When the new sample vehicle1070 is added to the vehicle class of the network at a node 1072, a sizeof the node 1072 increases to reflect the addition, as shown in thethird state 1064. Additionally, a topology of the network is adjusted,shown by changes in distances between the nodes, as a defining parametervector of the node 1072 and associated vehicle class is adjusted basedon an inclusion of the new sample vehicle 1070. A class-specific RULmodel error of the vehicle class represented by node 1072 may becalculated, based on RUL model predictions from each vehicle of thevehicle class represented by node 1072. As a result of theclass-specific RUL model error of the vehicle class not exceeding athreshold loss value, a new node is not created.

The fourth state 1065 shows the network at a later stage in time, when anew sample vehicle 1074 is added to a vehicle class represented by node1076. When the new sample vehicle 1074 is added, the GSOM algorithmdetects that node 1076 is the largest node of the network, and furtherdetects that a class-specific RUL model error of the vehicle classrepresented by node 1076 exceeds the threshold loss value. As a resultof the class-specific RUL model error exceeding the threshold lossvalue, a new vehicle class is created, represented by a new node 1078.The new node 1078 is inserted between node 1076 and a farthest node fromnode 1076, node 1080. As shown in the fifth state 1066, a number ofvehicles of the vehicle class represented by node 1076 may then bedivided between node 1076 and the new node 1078, where vehicles withparameter vectors most similar to a defining parameter vector of node1076 remain in the vehicle class represented by node 1076, and wherevehicles with parameter vectors most similar to a defining parametervector of the new node 1078 may be assigned to the vehicle classrepresented by the new node 1078. After the vehicles of node 1076 havebeen divided between node 1076 and node 1078, the topology of thenetwork is adjusted, shown by changes in distances between the nodes instate 1066, based on a recalculation of vehicle similarity withinvehicle classes and vehicle class size.

Thus, data from a connected vehicle population may be used to increasean accuracy of RUL models of a vehicle component of the connectedvehicle population. A cloud-based health monitoring system may dividethe connected vehicle population into a plurality of vehicle classesbased on a similarity or distance metric, where each vehicle class has aclass-specific RUL model that predicts the RUL of the vehicle componentof the respective vehicle class. The class-specific RUL model may becreated using clustering methods, and initially trained using groundtruth degradation data of the respective vehicle class. Theclass-specific RUL model may be subsequently updated as degradation datais collected by vehicles of the vehicle class via an FL strategy,whereby local RUL model data may be iteratively requested from randomlyselected portions of vehicles of the respective vehicle class and usedto update parameters of the class-specific RUL model. The parameters mayin turn be sent back to the randomly selected portion of vehicles of therespective vehicle class to update local RUL models of the randomlyselected portion of vehicles. By splitting the connected vehiclepopulation in to vehicle classes and training separate, class-specificRUL models, and using an output of the class specific RUL models toupdate parameters of local RUL models and/or a master RUL model for theconnected vehicle population, accurate RULs may be generated at avehicle level, a class level, and a vehicle population level. As aresult, component degradations at vehicles may be more accuratelypredicted, leading to a reduced number of total degradations in thefield, and improved management of a total population of components.

The technical effect of partitioning a vehicle population into vehicleclasses, and training and updating class-specific RUL models using an FLstrategy with connected vehicle data is that an accuracy of RUL modelsmay be increased at a vehicle level, a class level, and a vehiclepopulation level.

The disclosure also provides support for a method, comprising: dividinga population of vehicles of a connected vehicle population into aplurality of vehicle classes, for each vehicle class of the plurality ofvehicle classes, training a class-specific model of the vehicle class topredict a health status variable of a vehicle component included in thevehicle class based on labelled data from historic databases andcalibration data, and for each vehicle class of the plurality of vehicleclasses, using a first Federated Learning (FL) strategy to: requestlocal model data from each vehicle of a plurality of vehicles of thevehicle class, receive the local model data from the plurality ofvehicles, update the class-specific model of the vehicle class based onthe received local model data, and send updated parameters of theupdated, class-specific model to vehicles included in the vehicle classand further send instructions to retrain local models of the vehicleswith the updated parameters. In a first example of the method, theclass-specific model of the vehicle class is a remaining useful life(RUL) model that predicts an RUL of the vehicle component included inthe vehicle class. In a second example of the method, optionallyincluding the first example, the vehicle class is defined based onvehicle data including one or more of a vehicle similarity criterion, avehicle model, a vehicle feature package, a geographical region, anoperating condition, a type of vehicle usage, and a driver behavior. Ina third example of the method, optionally including one or both of thefirst and second examples, the method further comprises: generating theplurality of vehicle classes by: assigning vehicle parameter vectors tovehicles of the connected vehicle population based on the vehicle data,applying a clustering algorithm to the vehicle parameter vectors topartition the connected vehicle population into a set of vehicleclasses, and after updating parameters of a class-specific RUL model ofa vehicle class based on local RUL model data of a plurality of vehiclesof the vehicle class, in response to an error of the class-specific RULmodel exceeding a threshold error, repartitioning the connected vehiclepopulation into the set of vehicle classes by applying the clusteringalgorithm to the updated vehicle parameter vectors. In a fourth exampleof the method, optionally including one or more or each of the firstthrough third examples, the plurality of vehicle classes are generatedbased on a growing self-organized maps (GSOM) algorithm comprising:dividing the connected vehicle population into a small network ofvehicle classes, each vehicle class based on a defining parameter vectorof the vehicle class, assigning each vehicle of the connected vehiclepopulation to a vehicle class based on a proximity of a vehicleparameter vector of the vehicle to the defining parameter vector of thevehicle class, calculating a first statistic of each vehicle class, inresponse to a new vehicle being added to a selected vehicle class of thenetwork, calculating a second statistic of the selected vehicle class,and in response to a difference between the second statistic of theselected vehicle class and the first statistic of the selected vehicleclass exceeding a threshold value: creating a new vehicle class,assigning each vehicle of the selected vehicle class to either theselected vehicle class or the new vehicle class, based on a relativeproximity of the vehicle parameter vector of each vehicle of theselected vehicle class to either the defining parameter vector of theselected vehicle class or the defining parameter vector of the newvehicle class, respectively, and recalculating a defining parametervector of each vehicle class. In a fifth example of the method,optionally including one or more or each of the first through fourthexamples, the statistic is a vehicle population distribution of thevehicle class, and the threshold value is a threshold dissimilaritybetween the vehicle population distribution of the selected vehicleclass and the vehicle population distribution of the new vehicle classcalculated based on a distance metric. In a sixth example of the method,optionally including one or more or each of the first through fifthexamples, using the first FL strategy to update the class-specific modelof the vehicle class and send the updated, class-specific model to thevehicles included in the vehicle class further comprises: performing anFL learning session including a plurality of FL learning cycles, each FLlearning cycle comprising: iteratively adjusting parameters of theclass-specific RUL model based on data from local RUL models of a randomsubset of vehicles of the vehicle class, and sending the adjustedparameters of the class-specific RUL model to the random subset ofvehicles to retrain the local RUL models at the random subset ofvehicles based on the adjusted parameters of the class-specific RULmodel, until the parameters of the class-specific RUL models converge.In a seventh example of the method, optionally including one or more oreach of the first through sixth examples, iteratively adjustingparameters of the class-specific RUL model based on data from local RULmodels of a random subset of vehicles further comprises aggregatinglocal RUL model data and generating updated parameters of theclass-specific RUL model from the aggregated local RUL model data. In aneighth example of the method, optionally including one or more or eachof the first through seventh examples, the class-specific model isstored in a cloud-based health monitoring system wirelessly connected tothe connected vehicle population. In a ninth example of the method,optionally including one or more or each of the first through eighthexamples, a master RUL model of the vehicle component of the connectedvehicle population is updated by: training the master RUL model on aninitial training dataset including ground truth degradation data of thevehicle component, creating a set of learning federations, each learningfederation including local RUL models drawn from vehicles of theconnected vehicle population in accordance with a sampling strategy, andapplying a second FL strategy to the set of learning federations toupdate the master RUL model. In a tenth example of the method,optionally including one or more or each of the first through ninthexamples, the ground truth degradation data includes at least one ofsensor data from vehicles of the connected vehicle population,degradation data of the vehicle component, and repair data of thevehicle component. In a eleventh example of the method, optionallyincluding one or more or each of the first through tenth examples, thesampling strategy includes at least one of: a stratified samplingstrategy, where samples are drawn from homogeneous groups of vehicles ofthe connected vehicle population, a cluster sampling strategy, wheresamples are drawn from heterogeneous clusters of the vehicles of theconnected vehicle population, and a mixed sampling strategy, wheresamples are drawn from heterogeneous clusters of a homogeneous group ofthe vehicles of the connected vehicle population. In a twelfth exampleof the method, optionally including one or more or each of the firstthrough eleventh examples, applying the second FL strategy to the set oflearning federations to update the master RUL model includes averagingparameters of the master RUL model across a plurality of learningfederations.

The disclosure also provides support for a method for a vehicle,comprising: in response to detecting a degradation of a component of thevehicle, sending a first notice of service to a driver of the vehicle,updating a local remaining useful life (RUL) model of the componentbased on degradation data, based on an RUL predicted by the local RULmodel, sending a request to a cloud-based health monitoring system toinitiate Federated Learning (FL), and in response to receivingparameters of a class-specific RUL model of the component associatedwith a vehicle class of the vehicle from the cloud-based healthmonitoring system, updating local parameters of the local RUL model ofthe component based on the received parameters, and retraining the localRUL based on historical data of the vehicle. In a first example of themethod, in a first condition where the predicted RUL of the component isgreater than a minimum RUL, the request to initiate FL is sent, and in asecond condition where the predicted RUL of the component is not greaterthan the minimum RUL, the request to initiate FL is not sent. In asecond example of the method, optionally including the first example,updating the local parameters of the local RUL model of the componentbased on the received parameters further comprises either replacing thelocal parameters of the local RUL model with the received parameters orgenerating new parameters of the local RUL model as a function ofprevious parameters of the local RUL model and the received parameters.

The disclosure also provides support for a system, comprising: apopulation of vehicle components of a connected vehicle population, acloud-based server wirelessly connected to the population of vehiclecomponents, the cloud-based server including a processor andinstructions stored on non-transient memory that, when executed, causethe processor to: in response to receiving a request to initiateFederated Learning (FL) from a vehicle of the connected vehiclepopulation due to a degradation detected in a vehicle component of thevehicle: initiate an FL session to update parameters of a class-specificRUL model of a class of the vehicle component, based on aggregated dataof a plurality of local RUL models of vehicle components of vehicles ofthe class of the vehicle component, and transmit data of the updatedclass-specific RUL model to the vehicles of the class of the vehiclecomponent, the data including an updated predicted class-based RUL ofthe vehicle component. In a first example of the system, the aggregateddata of the plurality of local RUL models includes at least one ofparameters of the local RUL models, inputs to the local RUL models,outputs of the RUL models, and errors of the local RUL models. In asecond example of the system, optionally including the first example,initiating the FL session to update the parameters of the class-specificRUL model of the class of the degraded vehicle component and sending theupdated parameters of the class-specific RUL model to vehicles furthercomprises, for each FL cycle of a plurality of FL cycles of the FLsession, creating a learning federation including a pre-defined randomnumber of vehicles of the class, updating parameters of theclass-specific RUL model based on the aggregated data of the pluralityof local RUL models of the learning federation, transmitting the updatedparameters of the class-specific RUL model to the vehicles of thelearning federation for updating parameters of the local RUL models, andterminating the FL session when the parameters of the class-specific RULmodel converge with the parameters of the local RUL models to within athreshold convergence. In a third example of the system, optionallyincluding one or both of the first and second examples, updatingparameters of the class-specific RUL model based on the aggregated dataof the plurality of local RUL models of the learning federation furthercomprises training the class-specific RUL model to minimize a weightedsum of prediction errors of the local RUL models.

Note that the example control and estimation routines included hereincan be used with various engine and/or vehicle system configurations.The control methods and routines disclosed herein may be stored asexecutable instructions in non-transitory memory and may be carried outby the control system including the controller in combination with thevarious sensors, actuators, and other engine hardware. The specificroutines described herein may represent one or more of any number ofprocessing strategies such as event-driven, interrupt-driven,multi-tasking, multi-threading, and the like. As such, various actions,operations, and/or functions illustrated may be performed in thesequence illustrated, in parallel, or in some cases omitted. Likewise,the order of processing is not necessarily required to achieve thefeatures and advantages of the example embodiments described herein, butis provided for ease of illustration and description. One or more of theillustrated actions, operations, and/or functions may be repeatedlyperformed depending on the particular strategy being used. Further, thedescribed actions, operations, and/or functions may graphicallyrepresent code to be programmed into non-transitory memory of thecomputer readable storage medium in the engine control system, where thedescribed actions are carried out by executing the instructions in asystem including the various engine hardware components in combinationwith the electronic controller.

It will be appreciated that the configurations and routines disclosedherein are exemplary in nature, and that these specific embodiments arenot to be considered in a limiting sense, because numerous variationsare possible. For example, the above technology can be applied to V-6,I-4, I-6, V-12, opposed 4, and other engine types. Moreover, unlessexplicitly stated to the contrary, the terms “first,” “second,” “third,”and the like are not intended to denote any order, position, quantity,or importance, but rather are used merely as labels to distinguish oneelement from another. The subject matter of the present disclosureincludes all novel and non-obvious combinations and sub-combinations ofthe various systems and configurations, and other features, functions,and/or properties disclosed herein.

As used herein, the term “approximately” is construed to mean plus orminus five percent of the range unless otherwise specified.

The following claims particularly point out certain combinations andsub-combinations regarded as novel and non-obvious. These claims mayrefer to “an” element or “a first” element or the equivalent thereof.Such claims should be understood to include incorporation of one or moresuch elements, neither requiring nor excluding two or more suchelements. Other combinations and sub-combinations of the disclosedfeatures, functions, elements, and/or properties may be claimed throughamendment of the present claims or through presentation of new claims inthis or a related application. Such claims, whether broader, narrower,equal, or different in scope to the original claims, also are regardedas included within the subject matter of the present disclosure.

1. A method, comprising: dividing a population of vehicles of aconnected vehicle population into a plurality of vehicle classes; foreach vehicle class of the plurality of vehicle classes, training aclass-specific model of the vehicle class to predict a health statusvariable of a vehicle component included in the vehicle class based onlabelled data from historic databases and calibration data; and for eachvehicle class of the plurality of vehicle classes, using a firstFederated Learning (FL) strategy to: request local model data from eachvehicle of a plurality of vehicles of the vehicle class; receive thelocal model data from the plurality of vehicles; update theclass-specific model of the vehicle class based on the received localmodel data; and send updated parameters of the updated, class-specificmodel to vehicles included in the vehicle class and further sendinstructions to retrain local models of the vehicles with the updatedparameters.
 2. The method of claim 1, wherein the class-specific modelof the vehicle class is a remaining useful life (RUL) model thatpredicts an RUL of the vehicle component included in the vehicle class.3. The method of claim 2, wherein the vehicle class is defined based onvehicle data including one or more of a vehicle similarity criterion, avehicle model, a vehicle feature package, a geographical region, anoperating condition, a type of vehicle usage, and a driver behavior. 4.The method of claim 3, further comprising: generating the plurality ofvehicle classes by: assigning vehicle parameter vectors to vehicles ofthe connected vehicle population based on the vehicle data; applying aclustering algorithm to the vehicle parameter vectors to partition theconnected vehicle population into a set of vehicle classes; and afterupdating parameters of a class-specific RUL model of a vehicle classbased on local RUL model data of a plurality of vehicles of the vehicleclass, in response to an error of the class-specific RUL model exceedinga threshold error, repartitioning the connected vehicle population intothe set of vehicle classes by applying the clustering algorithm to theupdated vehicle parameter vectors.
 5. The method of claim 3, wherein theplurality of vehicle classes are generated based on a growingself-organized maps (GSOM) algorithm comprising: dividing the connectedvehicle population into a small network of vehicle classes, each vehicleclass based on a defining parameter vector of the vehicle class,assigning each vehicle of the connected vehicle population to a vehicleclass based on a proximity of a vehicle parameter vector of the vehicleto the defining parameter vector of the vehicle class; calculating afirst statistic of each vehicle class; in response to a new vehiclebeing added to a selected vehicle class of the network: calculating asecond statistic of the selected vehicle class; and in response to adifference between the second statistic of the selected vehicle classand the first statistic of the selected vehicle class exceeding athreshold value: creating a new vehicle class; assigning each vehicle ofthe selected vehicle class to either the selected vehicle class or thenew vehicle class, based on a relative proximity of the vehicleparameter vector of each vehicle of the selected vehicle class to eitherthe defining parameter vector of the selected vehicle class or thedefining parameter vector of the new vehicle class, respectively; andrecalculating a defining parameter vector of each vehicle class.
 6. Themethod of claim 5, wherein the statistic is a vehicle populationdistribution of the vehicle class, and the threshold value is athreshold dissimilarity between the vehicle population distribution ofthe selected vehicle class and the vehicle population distribution ofthe new vehicle class calculated based on a distance metric.
 7. Themethod of claim 2, wherein using the first FL strategy to update theclass-specific model of the vehicle class and send the updated,class-specific model to the vehicles included in the vehicle classfurther comprises: performing an FL learning session including aplurality of FL learning cycles, each FL learning cycle comprising:iteratively adjusting parameters of the class-specific RUL model basedon data from local RUL models of a random subset of vehicles of thevehicle class, and sending the adjusted parameters of the class-specificRUL model to the random subset of vehicles to retrain the local RULmodels at the random subset of vehicles based on the adjusted parametersof the class-specific RUL model, until the parameters of theclass-specific RUL models converge.
 8. The method of claim 7, whereiniteratively adjusting parameters of the class-specific RUL model basedon data from local RUL models of a random subset of vehicles furthercomprises aggregating local RUL model data and generating updatedparameters of the class-specific RUL model from the aggregated local RULmodel data.
 9. The method of claim 1, wherein the class-specific modelis stored in a cloud-based health monitoring system wirelessly connectedto the connected vehicle population.
 10. The method of claim 2, whereina master RUL model of the vehicle component of the connected vehiclepopulation is updated by: training the master RUL model on an initialtraining dataset including ground truth degradation data of the vehiclecomponent; creating a set of learning federations, each learningfederation including local RUL models drawn from vehicles of theconnected vehicle population in accordance with a sampling strategy; andapplying a second FL strategy to the set of learning federations toupdate the master RUL model.
 11. The method of claim 10, where theground truth degradation data includes at least one of sensor data fromvehicles of the connected vehicle population, degradation data of thevehicle component, and repair data of the vehicle component.
 12. Themethod of claim 10, where the sampling strategy includes at least oneof: a stratified sampling strategy, where samples are drawn fromhomogeneous groups of vehicles of the connected vehicle population; acluster sampling strategy, where samples are drawn from heterogeneousclusters of the vehicles of the connected vehicle population; and amixed sampling strategy, where samples are drawn from heterogeneousclusters of a homogeneous group of the vehicles of the connected vehiclepopulation.
 13. The method of claim 10, wherein applying the second FLstrategy to the set of learning federations to update the master RULmodel includes averaging parameters of the master RUL model across aplurality of learning federations.
 14. A method for a vehicle,comprising: in response to detecting a degradation of a component of thevehicle: sending a first notice of service to a driver of the vehicle;updating a local remaining useful life (RUL) model of the componentbased on degradation data; based on an RUL predicted by the local RULmodel, sending a request to a cloud-based health monitoring system toinitiate Federated Learning (FL); and in response to receivingparameters of a class-specific RUL model of the component associatedwith a vehicle class of the vehicle from the cloud-based healthmonitoring system, updating local parameters of the local RUL model ofthe component based on the received parameters; and retraining the localRUL based on historical data of the vehicle.
 15. The method of claim 14,wherein: in a first condition where the predicted RUL of the componentis greater than a minimum RUL, the request to initiate FL is sent; andin a second condition where the predicted RUL of the component is notgreater than the minimum RUL, the request to initiate FL is not sent.16. The method of claim 14, wherein updating the local parameters of thelocal RUL model of the component based on the received parametersfurther comprises either replacing the local parameters of the local RULmodel with the received parameters or generating new parameters of thelocal RUL model as a function of previous parameters of the local RULmodel and the received parameters.
 17. A system, comprising: apopulation of vehicle components of a connected vehicle population; acloud-based server wirelessly connected to the population of vehiclecomponents, the cloud-based server including a processor andinstructions stored on non-transient memory that, when executed, causethe processor to: in response to receiving a request to initiateFederated Learning (FL) from a vehicle of the connected vehiclepopulation due to a degradation detected in a vehicle component of thevehicle: initiate an FL session to update parameters of a class-specificRUL model of a class of the vehicle component, based on aggregated dataof a plurality of local RUL models of vehicle components of vehicles ofthe class of the vehicle component; and transmit data of the updatedclass-specific RUL model to the vehicles of the class of the vehiclecomponent, the data including an updated predicted class-based RUL ofthe vehicle component.
 18. The system of claim 17, wherein theaggregated data of the plurality of local RUL models includes at leastone of: parameters of the local RUL models; inputs to the local RULmodels; outputs of the RUL models; and errors of the local RUL models.19. The system of claim 17, wherein initiating the FL session to updatethe parameters of the class-specific RUL model of the class of thedegraded vehicle component and sending the updated parameters of theclass-specific RUL model to vehicles further comprises: for each FLcycle of a plurality of FL cycles of the FL session: creating a learningfederation including a pre-defined random number of vehicles of theclass; updating parameters of the class-specific RUL model based on theaggregated data of the plurality of local RUL models of the learningfederation; transmitting the updated parameters of the class-specificRUL model to the vehicles of the learning federation for updatingparameters of the local RUL models; and terminating the FL session whenthe parameters of the class-specific RUL model converge with theparameters of the local RUL models to within a threshold convergence.20. The system of claim 19, wherein updating parameters of theclass-specific RUL model based on the aggregated data of the pluralityof local RUL models of the learning federation further comprisestraining the class-specific RUL model to minimize a weighted sum ofprediction errors of the local RUL models.